A Cloud-based Architecture for the Internet of Things targeting Industrial Devices Remote Monitoring and Control

4th IFAC Symposium on Telematics Applications 4th IFAC Symposium on Telematics Applications 4th IFAC on Applications November 6-9, 2016. UFRGS, Porto Alegre, RS, Brazil 4th IFAC Symposium Symposium on Telematics Telematics Applications November 6-9, 2016. UFRGS, Porto Alegre, RS, Brazil Available online at www.sciencedirect.com November 6-9, 2016. UFRGS, Porto Alegre, RS, November 6-9, 2016. UFRGS, Porto Alegre, RS, Brazil Brazil

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A Cloud-based Architecture for the A Cloud-based Architecture for A Cloud-based Architecture for the the Internet of Things targeting Industrial Internet of Things targeting Industrial Internet of Things targetingand Industrial Devices Remote Monitoring Control Devices Remote Monitoring and Devices Remote Monitoring and Control Control Ademir Ademir Ademir Ademir

F. F. F. F.

da da da da

Silva, Silva, Silva, Silva,

Ricardo L. Ohta, Marcelo N. dos Santos, Ricardo Ohta, Marcelo N. dos Santos, Ricardo L. Marcelo Alecio P.L. Binotto Ricardo L.D.Ohta, Ohta, Marcelo N. N. dos dos Santos, Santos, Alecio P. P. D. D. Binotto Binotto Alecio Alecio P. D. Binotto IBM Research – Brazil IBM Research – Brazil Research – (ademir.ferreira,IBM rlohta, mnerys, abinotto)@br.ibm.com IBM Research – Brazil Brazil (ademir.ferreira, rlohta, rlohta, mnerys, mnerys, abinotto)@br.ibm.com abinotto)@br.ibm.com (ademir.ferreira, (ademir.ferreira, rlohta, mnerys, abinotto)@br.ibm.com

Abstract: The process of acquiring, analysing and managing data obtained by sensors and Abstract:inThe The process of acquiring,can analysing and managing managing data obtained by sensors and Abstract: process of and data by and actuators industrial environments benefit from Cloud-based platforms towards Abstract: The process of acquiring, acquiring, analysing analysing and modern managing data obtained obtained by sensors sensors anda actuators in industrial environments can benefit from modern Cloud-based platforms towards actuators in industrial industrial environments environments can benefit benefit from modern modern Cloud-based platforms towards complete implementation of the Industrie 4.0 concept. The analysis of hugeplatforms data sets towards producedaaa actuators in can from Cloud-based complete implementation of the Industrie 4.0 concept. The analysis of huge data sets produced complete implementation of the Industrie 4.0 concept. The analysis of huge data sets produced by these implementation sensors (Big Data) could allow 4.0 quick and accurate decision making. For produced example, complete of the Industrie concept. The analysis of huge data sets by these these sensors sensors (Big Data) Data) could allow quick quick and accurate accurate decision making. For example, by (Big could allow and decision making. For productivity improvements can be achieved by analysing device performance and degradation by these sensors (Big Data) could allow quick and accurate decision making. For example, example, productivity improvements can be achieved by analysing device performance and degradation productivity can analysing performance degradation for real-time improvements feedback on configuration and by optimization. This work proposesand a Cloud-based productivity improvements can be be achieved achieved by analysing device device performance and degradation for real-time real-time feedback feedback on configuration configuration and optimization. optimization. This work proposes proposes Cloud-based for on and work aaa Cloud-based architecture Internet Things (IoT) to This improve deployment of smart for real-time for feedback on of configuration andapplications optimization. This workthe proposes Cloud-based architecture for Internet of Things applications to improve the deployment of smart architecture for (IoT) applications to the of industrial systems based of on Things remote (IoT) monitoring and control. By using specific technologies architecture for Internet Internet of Things (IoT) applications to improve improve the deployment deployment of smart smart industrial assystems systems based on remote monitoring monitoring and control. control. By using using specific technologies industrial based on remote and By specific technologies available a service, we demonstrate the proposed architecture on an automated electric industrial systems based on remote monitoring and control. By using specific technologies available as as a service, we demonstrate demonstrate the proposed architecture on andata automated electric available a we the on electric induction motor use case. approach includes layers architecture for sensor network gathering, data available as a service, service, weThis demonstrate the proposed proposed architecture on an an automated automated electric induction motor use case. This approach includes layers for sensor network data gathering, data induction motor use case. This approach includes layers for sensor network data gathering, data transformation protocols, message queuing, real-time data analysis, reporting induction motorbetween use case.standard This approach includes layers for sensor network data gathering, data transformation between standard protocols, message queuing, real-time data analysis, reporting transformation between standard protocols, message queuing, real-time data analysis, reporting for further analysis, andstandard real-timeprotocols, control. Particularly, by using the proposed architecture, we transformation between message queuing, real-time data analysis, reporting for further further analysis, analysis, and real-time control. Particularly, by using the proposed architecture, we for real-time Particularly, by the architecture, we remotely controlled andcontrol. processed data produced by sensors and actuators coupled for furthermonitored, analysis, and and real-time control. Particularly, by using using the proposed proposed architecture, we remotely monitored, controlled and processed data produced by sensors and actuators coupled remotely monitored, controlled and processed produced sensors and coupled to the motor. Preliminary results indicate thisdata foundation canby support methods and remotely monitored, controlled and processed data produced by sensorspredictive and actuators actuators coupled to the the motor.ofPreliminary Preliminary results indicate this foundation can support predictive methods and to indicate this can management automatedresults systems in the Industrie 4.0 context. to the motor. motor. Preliminary results indicate this foundation foundation can support support predictive predictive methods methods and and management of of automated automated systems systems in in the the Industrie Industrie 4.0 context. management 4.0 context. management of automated systems in the Industrie 4.0 context. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Internet of Things (IoT); Industry 4.0; Tele-maintenance Keywords: Internet Internet of of Things Things (IoT); (IoT); Industry Industry 4.0; 4.0; Tele-maintenance Tele-maintenance Keywords: Keywords: Internet of Things (IoT); Industry 4.0; Tele-maintenance 1. INTRODUCTION actuators and suitable data systems, but also the per1. INTRODUCTION actuators condition and suitable data systems, but also the per1. actuators and data systems, but the formance of the evolved devices. Additionally, 1. INTRODUCTION INTRODUCTION actuators and suitable suitable data systems, but also also the perperformance condition of the evolved devices. Additionally, formance condition of evolved devices.of Additionally, Additionally, this scenario can generate huge amounts data during formance condition of the the evolved devices. this scenario scenario can generate huge amounts amounts of data during The transformation of society demands towards technolog- this generate huge of hundreds data during during operation timecan due to potential or this scenario can generate huge presence amounts of data The and transformation of society demands towards technologThe transformation of technologoperation time due to potential presence of hundreds or ical cheaper products anddemands services istowards a strong stimulus operation The transformation of society society demands towards technologtime due to potential presence of hundreds or even thousands of sensors, considered asofBig Data by operation time due to potential presence hundreds or ical and cheaper products and services is a strong stimulus ical and and is even thousands thousands of sensors, sensors, considered ascan BigbeData Data by for improvements on industrial processes. The stimulus develop- even ical and cheaper cheaper products products and services services is aa strong strong stimulus of considered as Big by Manyika et al. (2011). Analytic models applied thousands of sensors, considered as Big Data by for improvements improvements on industrial processes. The develop- even for on processes. developManyika et al. (2011). (2011). Analytic models can beoperation applied ment of connected devices and instrumented environments for improvements on industrial industrial processes. The The develop- Manyika al. models can on these et data, to optimize industrial Manyika et al. helping (2011). Analytic Analytic models can be be applied applied ment of connected devices and instrumented environments ment of connected devices and instrumented environments on these data, helping to optimize industrial operation combined with available of data environments analysis and on ment of connected devicestechniques and instrumented these data, helping to optimize industrial operation procedures and helping extending life cycle of the monitored on these data, to the optimize industrial operation combined provided with available techniques ofconsolidate data analysis and combined with techniques and proceduresThese and extending extending thegoals life cycle cycle of the monitored monitored cognition the conditions what is procedures combined with available available techniquestoof of data data analysis analysis and and the life the machines. are the key and of explain why this procedures and extending the life cycle of the monitored cognition provided the conditions to consolidate what is cognition provided the consolidate what is machines. revolution These are are wave the key key goals and explain explain why why this this now known as Internet of Thingsto According cognition provided the conditions conditions to(IoT). consolidate what to is machines. These the and industrial is ingoals ascension. machines. These are the key goals and explain why this now known as Internet of Things (IoT). According to now known as Internet of Things (IoT). According to industrial revolution wave is in ascension. Hermann et al. (2015), interoperability, modularity, disnow known as Internet of Things (IoT). According to industrial revolution wave is revolution is in in ascension. ascension. Hermann processing et al. (2015), (2015), interoperability, modularity, dis- industrial Hermann et interoperability, disarticle proposeswave an architecture for rapid time-totributed of huge amounts of modularity, data produced Hermann et al. al. (2015), interoperability, modularity, dis- This This article proposes an architecture for rapid time-totime-totributed processing of huge huge amounts of data data produced article proposes for tributed processing amounts market deployment anarchitecture industrial automation system by these heterogeneous devices, and theof with This This article proposesofan an architecture for rapid rapid time-totributed processing of of huge amounts ofintegration data produced produced market deployment of an industrial automation system by these heterogeneous devices, and the integration with market deployment of an industrial automation system by these heterogeneous devices, and the integration with with remote monitoring and control services. It is an onother systems for industrial processes are still research market deployment of an industrial automation by these heterogeneous devices, and the integration with with remote monitoring and control services. It is system an onother systems for industrial processes are still research with remote monitoring and control services. It is other systems for processes still demand platform based on Cloud Computing challenges, including in the core context are of Industrie 4.0. with remote monitoring and control services.underpinned It is an an ononother systems for industrial industrial processes are still research research demand platform based on Cloud Computing underpinned challenges, including including in in the the core core context context of of Industrie Industrie 4.0. 4.0. demand platform based on Cloud Computing underpinned challenges, by standard industrial relying underpinned on IoT condemand platform based protocols on Cloud and Computing challenges, including in the core context of Industrie 4.0. by standard standard industrial protocols protocols and relying on IoT conIn an industry, manufacturing processes are the core by industrial andonrelying relying on IoT conconcepts. It is considered services Cloud on Computing standard industrial that protocols and IoT In an an industry, manufacturing processes are the core core by In manufacturing processes are the cepts. It is considered that services on Cloud Computing activity and, among several equipments, oneare omnipresent In an industry, industry, manufacturing processes the core cepts. It is considered that services on Cloud Computing environment can consume data on-demand (generated by cepts. It is considered that services on Cloud Computing activity and, among several equipments, one omnipresent activity and, several equipments, one environment can consume consume data on-demand (generated by is the electrical motor. This can easily explain why 40% of environment activity and, among among several equipments, one omnipresent omnipresent can (generated by sensors), execute analysis data and on-demand send (real-time) control environment can consume data on-demand (generated by is the electrical motor. This can easily explain why 40% of is electrical motor. This explain why of sensors),This execute analysis and sendaccordingly, (real-time)not control thethe electrical power consumption is related to motors, thus is the electrical motor. This can can easily easily explain why 40% 40% of sensors), execute analysis and send (real-time) control actions. allows taking actions only sensors), execute analysis and send (real-time) control the electrical electrical power power consumption is related related to motors, thus the to actions. This allows taking actions actions accordingly, not only representing of consumption the electrical is consumed bythus the actions. the electrical 2/3 power consumption ispower related to motors, motors, thus allows taking not with the This system conditions but also including actions. This allows takinginformation, actions accordingly, accordingly, not only only representing 2/3 of the electrical power consumed by the representing 2/3 of electrical power consumed by the with the system conditions information, but also including industry sector electric motor is representing 2/3(Saidur of the the (2010)). electricalThe power consumed byused the with the system conditions information, but also including data provided byconditions external sources, such asbut weather forecast with the system information, also including industry sector (Saidur (2010)). The electric motor is used industry sector (Saidur (2010)). The electric motor is used data provided provided by external sources, such as as weather forecast in severalsector configurations of the automation an data industry (Saidur (2010)). The electricprocess. motor isAs used by sources, such forecast data. This capability provides insights could influence provided by external external sources, suchthat as weather weather forecast in several several configurations configurations of the the automation process. As As an data in process. data.operation This capability capability provides insights that could influence influence example, case studyof an exhaustion in several our configurations offocused the automation automation process.system, As an an data. This provides insights that could the conditions of a certain industrial activity data. This capability provides insights that could influence example, our case study focused an exhaustion exhaustion system, example, our focused an the operation conditions of aa certain certain industrial activity which is critical to study keep the working within the example, our case case study focused an environment exhaustion system, system, operation conditions of industrial activity in this case, the exhaustion system. Even though the operation conditions of a certain industrial activity which is is critical to keep the working environment within the which to the environment within - in in this this case, case, the exhaustion system. Even though the acceptable conditions in several phases of the production which is critical critical to keep keep the working working environment within --proposed the exhaustion system. Even though the infrastructure is generic enough to be applied in in this case, the exhaustion system. Even though the acceptable conditions in several phases of the production acceptable in phases of infrastructure is scenarios, generic enough enough to be be applied appliedthe in chain (e.g., conditions from electronic industry, passing to production coal-based proposed acceptable conditions in several several phases of the the production infrastructure is generic to in aproposed wide array of industrial we demonstrated proposed infrastructure is generic enough to be applied in chain (e.g., from electronic industry, passing to coal-based chain (e.g., electronic industry, to a wide wide array array of industrial industrial scenarios, we demonstrated the thermoelectric electric power plant, topassing grain warehousing). chain (e.g., from from electronic industry, passing to coal-based coal-based aadescribed of scenarios, we demonstrated the concepts for an industrial electric motor in wide array of industrial scenarios, we demonstrated the thermoelectric electric electric power power plant, plant, to to grain warehousing). warehousing). described thermoelectric concepts for an an industrial industrial electric motor in the the thermoelectric electric to grain grain warehousing). for electric motor context ofconcepts a machine monitored and controlled by described concepts forto anbe industrial electric motor in in the Currently, not only thepower setupplant, and operation of automated described context of a machine to be monitored and controlled by context of a machine to be monitored and controlled by Currently, not only the setup and operation of automated Currently, not only the setup and operation of automated context of a machine to be monitored and controlled by industrial systems can be remotely controlled by sensors, Currently, not only the setup and operation of automated industrial systems can be remotely controlled by sensors, industrial industrial systems systems can can be be remotely remotely controlled controlled by by sensors, sensors, Copyright © 2016, 2016 IFAC 108Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright 2016 IFAC 108 Copyright © 2016 IFAC 108 Peer review© of International Federation of Automatic Copyright ©under 2016 responsibility IFAC 108Control. 10.1016/j.ifacol.2016.11.137

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sensors and actuators in an exhaustion system managed by such a Cloud platform. The work is structured as follows. Section 2 presents a background. Section 3 details the proposed architecture along with protocols and technologies used to develop our IoT-to-Cloud infrastructure. Section 4 describes the use of the infrastructure in a real industrial automation case study. Section 5 gives an overview of related work in comparison to our proposal. Finally, conclusions are exposed in Section 6. 2. BACKGROUND Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). According to Mell and Grance (2011), Infrastructure, Platforms, and Services can be rapidly provisioned and released with minimal management effort or service provider interaction; it also has essential characteristics (on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service), service models (IaaS, PaaS, and SaaS), and deployment models of infrastructure (private, community, public and hybrid) that an organization needs to understand well in order to benefit and improve its environment. IaaS (Infrastructure as a Service) provides infrastructure capabilities to deploy and run software in general. PaaS (Platform as a Service) provides capabilities to develop applications and deployment in the Cloud. SaaS (Software as a Service) provides resources to use software applications through a thin-client interface, such as via Web Browsers or Application Program Interface (API). Currently, a number of research studies are investigating the use of Cloud Computing resources as an alternative to the expensive on-premise clusters (CAPEX investments). In fact, the Cloud approach is a technology and a business model that allows users to allocate resources ondemand. Cloud providers make profit by placing workloads of several clients together, i.e., leveraging the economies of scale. A wide number of applications are being moved to the Cloud, including sensitive financial, scientific and engineering applications, like industrial and IoT-based applications. Initially, the computing resources were only virtualized (one physical processor shared by several virtual ones), but a few providers are now allowing users to rent physical machines in a private and secured microCloud. This way, what was initially created to host web applications recently shifted to other types of workloads that require more privacy and processing power. A Cloud environment can provide several operational benefits to industries, particularly the IoT, such as: installation costs reduction (i.e., pay-per-use), flexibility to provision specific infrastructure (hardware and software), ease expansion of the infrastructure when necessary, among others. In this work, we particularly use the IBM Watson IoT Platform 1 and the PaaS capabilities of IBM Bluemix 2 , both from the IBM provider, as an implementation example of the proposed architecture. The IBM 1 2

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Watson IoT Platform makes it easier for any device to publish data to a back-end message broker and also to receive control messages from other devices or IoT applications. On the other hand, the IBM Bluemix allows to easily create IoT applications that communicates to the IBM Watson IoT Platform, so that these applications can consume data coming from devices or even send control messages to them. 3. A CLOUD ARCHITECTURE FOR DEVICE MONITORING AND CONTROL The proposed Cloud architecture for monitoring and controlling devices is composed by three main layers (Figure 1): wireless device network; gateway; and, Cloud service. The wireless device network corresponds to the set of sensors and actuators attached to the system (e.g., exhaust system) to be monitored and controlled. The gateway converts messages coming from the wireless network to the Cloud protocol. The transformed messages are consumed by the Cloud Service, which in turn, after evaluating overall systems conditions (including data obtained by other systems such as a weather forecast system), can send back commands to the actuators on the controlled network.

Fig. 1. Architecture overview Figure 2 details the components of this architecture, which are described in the next subsections. • Devices: a network of sensors and actuators for data gathering and control; • Gateway: performs data transformation to adapt acquired device data to the enterprise Cloud environment; • Cloud Services: · Broker: receives and organizes data streams coming from the gateway or mobile and desktop clients and dispatches them the message queue, that performs data cleansing, transformation, and queueing; besides, it routes queued messages to be processed by the other architecture modules; · Database: stores received data; · Front-end server: supports data analytics visualization, reporting, and recommendations for user actions; · Event managers: supports rules authoring, and performs real-time data analysis to deal with urgent event handling, firing alerts if conditions are met. 3.1 Wireless Device Network The sensor network is based on IEEE 802.15.4. This standard allows the devices to: (i) create routes for multi-

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received, although some delay can be created due to queueing. This module also routes the messages to services that are available to process new message incomes, considering a cluster of replicated services. The Broker & Message Queue works to support scalability due to the increasing number of devices. The data are stored on a Database.

Fig. 2. Architecture details hop communication; (ii) use cryptography mechanisms, e.g., AES-128 (Advanced Encryption Standard 128 bits); (iii) use beacons to maintain communication scheduling with low energy consumption, alternating on hibernation and processing cycles. We propose the use of 6LoWPAN 3 (IPv6 over Low power Wireless Personal Area Networks) protocol. 6LoWPAN is an adaptation of IPv6 for low resources dedicated devices, which have limited capabilities in terms of processing, RAM and storage. It supports addressing devices in the wireless network on IPv6 and also has a native support for IPsec. In order to scale the number of nodes in the network, we propose the use of the messaging communication paradigm publish and subscribe. The method publish / subscribe is based on the exchange of asynchronous messages, providing decoupling between system elements, low data overhead between messages producers and consumers, flexibility and scalability for applications, thus, under these circumstances widely used in techniques of IoT (Sheltami et al., 2016). 3.2 Gateway The Gateway is responsible to transfer data between the device network and the LAN (Local Area Network), reaching the Cloud service. The gateway is a device composed by adapters that provides both sides connectivity, e.g., it connects IEEE 802.15.4 and IEEE 802.11 protocols. In the device network, data is captured by the several network devices and routed to the gateway. The gateway transforms 6LoWPAN messages to the IPv6 format so that the messages reach the LAN. It can also have mechanisms for tunneling IPv6 over IPv4. This tunneling is required for networks which do not implement IPv6. 3.3 The Cloud Service The Cloud Service is responsible to receive data from industrial devices, users devices (e.g. mobiles) and execute data analytic algorithms to monitor the industrial devices, eventually producing commands to be sent back to the device network. This service also supports elasticity when new industrial equipments are added to the network and other on-demand features.

The Event Manager module processes all messages considering defined event rules. For instance, the Event Manager evaluates messages coming from a motor’s temperature sensor to check if temperature values are above a specific threshold, which is defined by the motor specification, environment and load conditions. The Event Rule Manager allows rule authoring. Rules might fire alarms and warnings, and trigger actions to be executed automatically by systems or manually by operators. The Visualization & Control module provides realtime views about the health of the several devices in a plant. This module also provides historical views, expected working patterns and probabilities of fail according to analytical models. Both i) web applications and ii) mobile applications can be employed to allow a user to monitor the device network and to act upon it, possibly due to an alert that has been triggered or because new configurations that should be set on the devices. For instance, the app can provide notifications fired by the Event Manager and then, the operators are able to further analyse the situation and intervene if needed. The Analytic Models provide information that allows understanding how the automated system is working and forecast future conditions based on industrial device’ historical working patterns. For example, through combining mathematical models with artificial intelligence and machine learning, Analytic Models detect motor individual working pattern according to power and application characteristics. The Recommendation module provides guidance about which part should be replaced and what is the procedure to perform the replacement. The Recommendation module also consumes information generated by the Analytics Models, e.g., to recommend the replacement of similar industrial device following an investment threshold due to a maintenance schedule. The Report module presents performance indicators, summary of past events, list of underused devices. This information may help facilities managers in their decision making process. 4. CASE STUDY: AN INDUSTRIAL EXHAUSTER

The Broker & Message Queue module is responsible to manage income messages. It receives and organizes the stream of messages coming from the industrial devices and users devices. It allows the processing of all messages 3

Fig. 3. Setup overview

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The proposed architecture was evaluated using a number of industrial devices connected to simulate an exhauster system. This set consisted of a 10hp three-phase motor coupled to a propeller, an frequency inverter for speed adjustment and motor drive, and a device with a hydrocarbon sensor for the monitoring of ambient gas concentration. It was also connected to this device a tri-axial accelerometer targeting vibration analysis; and a hygrometer sensor to collect engine temperature and humidity. Figure 3 depicts the system. Its evaluation had two distinct goals: monitoring the devices operating condition; and the remote monitoring and control on the exhausting system based on acquired parameters (telematics) and also on external data sources information (e.g., for production optimization) integrated to the our cloud analysing service. 4.1 Operation Monitoring In the test setup, we used an Arduino board 4 as a sensor data node together with a Raspberry Pi 5 working as a gateway. As a proof of concept, we have implemented the publish/subscribe paradigm by MQTT protocol (Message Queuing Telemetry Transport). In order to send data to the Cloud, the gateway connects to an MQTT broker and publishes MQTT messages periodically. In our test setup, the communication between the sensor data node and the gateway was built using a XBee Pro S1 Wireless shield. We observed that some typical data measured in engine monitoring, like temperature and humidity, have a steady behaviour. It is not expected that amplitudes of these properties can vary in a short period of time. In this sense, a sampling rate of about 1Hz would be enough to keep track of variations for such properties. On the other hand, when measuring acceleration of a stationary body, e.g., to infer the vibration of a motor, an increasing sampling rate may be needed, according to Lee et al. (2014). This way, to obtain data regarding the engine health status, two sensors were attached on the engine frame (connected to the data sensor node): a triple axis accelerometer (MMA8452); and, thermohygrometer (temperature and humidity) sensor (DHT11).

while still maintaining the higher frequency sampling. The decision on which approach to follow depends on the need for the Cloud to receive all raw data, the availability of computing resources on the gateway, and the characteristics of the underlying Cloud infrastructure for routing the IoT messages. We have taken the last approach, batch submit, sending 20 data packets by second with 10 samples of XYZ acceleration. The collected data was formatted in a JSON 6 representation and sent to the IoT application, so the data could be processed, stored and displayed. Once data gets published to the MQTT broker on a given topic, the Event Manager consumes it, since the Event Manager is subscribed in the same topic of the MQTT messages sent by the gateway. As a proof of concept, we have implemented an IoT application using Node-RED 7 and hosted it in IBM Bluemix. This application leverages Cloudant, 8 a NoSQL database, to store data samples being received. Also, the application exposes a RESTful API so that data can be queried and consumed by external applications. Figure 4 depicts preliminary results generated by our prototype setup. These verifications were elaborated in a scenario synthesizing four different situations: (i) Motor off; (ii) Motor On; (iii) Motor One Phase Fail; and (iv) Motor without Ventilation. This situations typically occur in a motor setup, and, for our experiment, each situation lasted one hour during the monitoring period.

The data acquisition method is executed in Arduino board every 5 milliseconds for the accelerometer and every 1 second for the thermohygrometer. This sampling frequency might avoid the detection of certain problems, such as damaged bearings, but it still covers the most common vibration problems to be tackled in a prototype, e.g., unbalanced or misalignment rotor shaft and looseness of stator support. The main concern was to validate the proposed solution environment. In order to send acquired data to the Cloud service, multiple approaches can be taken depending on goals/requirements and the Cloud infrastructure being used. For example, the gateway device can use some of its computing power to gather samples with high frequency and compute a vibration indicator based on a set of samples. Then, it could send a vibration indicator with a lower frequency, like 1Hz. This indicator may hold all the useful information extracted from the samples. Another approach would be to send a batch of samples in a single message, so that the overall number of messages are smaller than the number of samples,

Fig. 4. Tests results

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The Motor Off situation was developed to obtain the pattern of frequencies due to natural vibration (considered as a “learning phase”). In this case, high amplitude was detect on 11Hz. This pattern was also verified in all other tests. In the Motor On situation, we got the signature of normal operation for this particular motor in terms of frequency response. Besides the natural vibration, it was possible to detect a frequency on 6Hz and a set of frequencies on 15Hz. The motor was limited to rotate at a very low speed (40 RPM) for experiments of monitoring the operation condition. Thus, these specific frequencies were observed by this rotation speed behaviour. Motor One Phase Fail situation represents the scenario in which a motor coil burns or there is a fail in one of the three power source phases. In our tests, the motor itself stopped when performing this behaviour. This situation would be very challenging to detect. A higher sample rate could help in 6 8

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detecting high frequency components. In the Motor without Ventilation test, it was possible to detect the behaviour of an important amplitude increase on frequencies related to the Motor On test. All these data were made available through the architecture described on Section 3. During this ongoing research, preliminary results indicated that normal and abnormal behaviours could be detected just in time by using the proposed approach in a non intrusive way, for instance without any intervention on motor usage. 4.2 Exausting System Remote Monitoring In the experiment, the motor moves a propeller that take out gases from the environment, simulating an industrial exhaust system. In our case, alcohol vapour was used as the monitored gas and its concentration was used for controlling the exhaust system. When the concentration of alcohol reached a threshold, the engine was actioned removing the environment parts of alcohol vapour. The system was configured to drive the motor by the frequency inverter in a percentage of total power (1800 RPM), depending on the gas concentration rate in the environment recorded by the cloud service. The Arduino board, in this exhaust automation experiments, receives data from the inverter, about engine speed and electric power consumed, and sends commands to control it. In addition, it gathers data from the hydrocarbon sensor (MQ-3), accelerometer, thermohygrometer and also controlled a fan. The fan was responsible for blowing alcohol vapour in the system, with an alcohol bottle next to it. In order to demonstrate the capabilities of system to fire events when detect abnormal behaviours, an a e-mail sender was implemented in Event Manager module to notify user managers when the hydrocarbon concentration was higher than the normal operating rate (e.g., when the motor and/or propellant submit a defect message). Compared to a local control of exhaust, the external information integrated to our cloud service may assist in optimizing the production process. For example, in scenarios that the automated system blows hot air in a production plant (grain drying silos), the ventilation control system may receive weather forecast data about the area, thus improving energy efficiency. A mobile application was also developed targeting the operators. The operator can remotely turn on/off and change the speed of the exhauster. Other checks can also be performed, like information regarding to the concentration of hydrocarbons, temperature, humidity and also see the engine operating conditions by a live stream from external data to exhaust system using an IP camera, figure 5. A web application was also developed. Similarly to the mobile app, a user can check the historical properties of the motor, the accelerometer data, a live video stream of the motor, and also the values being measured by the hydrocarbonates sensor. Both web and mobile applications make use of RESTful calls to the cloud service, and use MQTT clients to communicate directly to the IoT platform. 5. RELATED WORK Cloud computing employed in industrial environments can bring benefits, but also poses challenges as data storage 112

Fig. 5. Mobile Application of Big Data, according to Jestratjew and Kwiecie´ n (2010). The Cloud manufacturing, which should correspond to the application of Cloud in the industrial context, described by Xu (2012), focuses on enhancing flexibility of the industrial environment, i.e., optimization of supply chain to maximize the throughput or the number of different products that it can be factored. However, it does not consider the use of on demand services for industrial automation and preventive maintenance purposes nor it is an architecture for IoT-to-Cloud, which is the purpose of our work. Analyzing Cloud as stand alone execution platform for engineering applications, Napper and Bientinesi Napper and Bientinesi (2009) and Ostermann et al. (2009) evaluated some particular workloads on the Amazon EC2 Cloud. Both works concluded that such Cloud services are effective for common workload and need an order of magnitude in performance improvement to better serve some particular high intensive scientific workload. In our work, however, we are dealing with low intensive, but in huge amounts of data produced by sensors. It is actually hard to evaluate one provider or another, but they are evolving to offer private and specialized infrastructures. In our study, we evaluated the IBM Watson IoT Platform and IBM Bluemix. Preliminary results indicate this environment as cost-effective in budget and performance. e-Maintenance comprises the delivery of information in the most convenient format (web/mobile) to allow effective maintenance, demonstrated by Muller et al. (2008). Specifically targeting industrial electric motors, there are already methods that merge both signal processing and artificial intelligence to detect defects. In general, they consider the disturbances in the electrical current and vibratory behavior for defining the defect. Some studies, using prediction analytical models, concluded that it is possible to anticipate the failure of electric motors, according to Lee et al. (2014). Besides, it is a trend to use mobile devices to support such maintenance activities, as said by Pistofidis and Emmanouilidis (2012). Additionally, machine learning and data mining techniques can be used to generate intelligent models for e-maintenance to support decision-making. The analysis and combination of collected data is still a topic of intense research. Trends in this area go beyond basic evaluation of electric motor power consumption, vibration, temperature and frequency as demonstrated by Ramamurthy et al. (2007). It can include the utilization of multiple sensors combined with multiple sensor data fusion, Jardine et al. (2006) and the optimal usage of this information in the context of maintenance management, Galar et al. (2012). Our work do not provide a particular analytical model, but its focus is on

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a solid architecture that enables remote monitoring and controlling of IoT-to-Cloud devices, including via mobile, towards a rapid prototyping of different case studies, like the exhaust automation. Focusing on the case study, some works targeting the Wireless Sensor Network (WSN) area include the monitoring of ball screw, Lee et al. (2013) and electric motors, Ramamurthy et al. (2007). Some focus on the application of WSN monitoring in manufacturing processes, such generic industrial processes, Zhao (2011). Those works are related to the first layer of our architecture. These solutions are scenario specific and, therefore, difficult to apply in other situations. Our proposed approach targets a flexible architecture that can be applied to a wide range of IoT applications rather just to a specific device. 6. CONCLUSION We have shown that the allocation of Cloud resources and the aforementioned solution for IoT industrial monitoring and controlling applications can be used as an alternative to the current available infrastructure solutions. It fosters real-time maintenance routines, implements the foundation for detection of abnormal behaviour and equipment failure, data fusion powered by cloud services, and subsequent control of the industrial plant. Preliminary experiments demonstrated not just the implementation of the proposed approach using specific technologies, but also its use for the sensors/actuators case study. Benefits would come also in less investment in infrastructure, energy, no real state occupation, and easiness of IoT prototyping using IaaS and PaaS, i.e., faster time-to-market. The presented solution is in line with the Industrie 4.0 model and allows the activity to be run as “pay as you go” for small businesses by providing quick and easy deployment in the industrial environment. Although our implementation focuses a single specific system, the proposed architecture can be extended to a wide range of IoT-based applications (e.g., healthcare, intelligent environment and living, traffic monitoring, among others). It is important to highlight that the proposed architecture is intended to complement existing automation systems (e.g., SCADA systems) by demonstrating the feasibility to reach the last mile not only in modern industrial parks, but also the legacy of industrial parks. Of course, there remain aspects like security and privacy that should be addressed by cloud management systems and network protocols. They should assure that data is only accessed and devices controlled by authorized users this is key in a complex scenario of real plants. Finally, we beleive that providing such an architecture that enables analitical services for preventive health remote monitoring is an important step towards a greenfield approach. ACKNOWLEDGEMENTS This work has been partially supported by FINEP/MCTI under grant no. 03.14.0062.00. REFERENCES Galar, D., Palo, M., Van Horenbeek, A., and Pintelon, L. (2012). Integration of disparate data sources to 113

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