Internet of Things architecture in cloud computing for Enhanced Living Environments

Stylianos Balampanis1, Stelios Sotiriadis1,2, Euripides Petrakis1 1 School of Electronic and Computer Engineering,

Technical University of Crete, Chania, Greece. 2 The Edward Rogers Sr. Department of Electrical and Computer Engineering,

University of Toronto, Toronto, Canada sbalampanis@gmail.com, s.sotiriadis@utoronto.ca, petrakis@intelligence.tuc.gr

Abstract

The use of Internet of Things (IoT) devices delivers significant benefits for storing sensing data that could be utilized intelligently for multiple purposes. A major concern in enhanced living environments (ELE) is the use of sensors in an efficient manner for daily usage including collection, monitor and storage of data in order to improve users’ quality of life. In this article, we present an IoT cloud architecture that allows connection of users, their devices and the cloud system in a modular way, by separating modalities into different cloud services that are deployed independently. We focus on e-health domain and we present a use case of a user-patient monitoring for enhanced living using motion sensing devices. Our architecture provides significant benefits such as reduced costs and real time data monitoring aiming to improve patient experience. In particular, within rehabilitation environments, the system allows proactive treatment, real time prevention with continuous monitoring of users and improvement of overall treatment process by allowing access of caregivers to real time user information.

Keywords: Internet of Things, cloud computing, enhanced living environments, e-health, motion sensor data collection.

1. Introduction

The concept of cloud computing gets significant attention during the latest years and many companies recognized the advantages and the impact can have in people’s life. Today, it is evolved to an important technology for application developers and end users by allowing on demand and remote resource access. Cloud allows real time data collection and analysis in an efficient way by offering an infinitive view of resources, remote data management, easy access and economic benefits [14]. Over the years, the concept of modular services, also referred as future Internet (FI) [6] and IBM “microservices” [9] has been utilized widely as functional components of large and complex applications that are more easily configurable, monitored and updatable. In particular, such services are available by different cloud platform providers such as IBM [10], Amazon EC21 and FIWARE2. In this work we focused on FIWARE that provides cloud services to build novel FI applications that are use generic services called generic enablers (GEs). It offers open specification for services that could be spread at different geographically locations (and hosted in various nodes-namely FIWARE Lab

1https://aws.amazon.com/ec2/2 https://account.lab.fiware.org

nodes), and are available for utilization over the Internet. The services follow the form of service oriented architecture that allows REST based communication [11].

In parallel, the emergence of the Internet of Things (IoT) that involves sensors embedded to every day devices promotes monitoring data produced by humans or by the environment in an automatic way [2]. The correlation of cloud computing and IoT arises some new opportunities for wide usage of such data, since new applications could be developed and impact peoples’ everyday life [4], [14], [15]. The development of such applications that are using cloud resources becomes more easy by using scalable storage that could increase capacity and performance by adding new storage nodes dynamically. In addition, the high bandwidth data transmission speed and real time analysis makes it even more attractive.

In this article we propose a generic IoT architecture and we present a motion sensing cloud service to assist patients, derived from it. The fundamental idea is that placing such sensors in in enhanced living environments (ELE) will provide patient protection from accidents (i.e. elderly falls) and monitoring performed by caregiving staff without requiring their presence. In particular, they can monitor and create predefined movements for patients that are in a rehabilitation phase. We are motivated by the motion sensor data collection system presented in [3], [13] and [13] where data is collected according to an event based architecture that includes constant updates for requiring assistance in order to help the patient.

The use case includes monitoring of patients who have suffered a trauma (i.e. in knee). In particular, the patient performs a particular exercise for moving the knee while a special user (physician) evaluates the results of the users’ success rate and records his/her progress. The exercise features (rate, time performed etc.) is recorded in real time so the patient physician can observe remotely the recovery progress. The system is always set by the user with administrator permissions and its role is to recognize the new sensors system with a unique code, the patients’ places (i.e. rooms), delete sensors and change the thresholds of the exercise (i.e. motion movement depending on the position of the patient).

To implement the service, we focus on RESTFul architecture, and we deploy it in an OpenStack cloud system that is an open source software for creating clouds. The solution is based on microservices as building blocks of our proposed architecture. The advantages include services’ reusability, improved fault tolerance, easy distribution of newer versions, decoupling of services thus easy management. We anticipate that the “decoupling of system components from the application logic” is more flexible, for example in case of integrating a new system, it will not require changes to the internal procedures of the service. Having said that, Section 2 presents the problem area, Section 3 the conceptual model, Section 4 the Motion sensors in ELE using cloud computing system and Section 5 the conclusions of our work.

2. Related technologies

Cloud computing systems include infrastructure and software that could be delivered in the form of remote services on a pay as you go pricing model, and this has been defined as the next step of the Internet evolution. Today, another promising technology is the edge computing that pushes clouds away from its

logical network forming the so-called fog computing3 that in turn expands clouds functionality allowing business logic and process management to be executed as near to the actual data source (i.e. a laptop or a smart-phone). This characterizes an alternative view of clouds where services can be extended to user premises and utilized directly in users’ personal devices. Fog computing could offer cloud technological know-how for remote data storage and management, while local data processing will facilitate a self-adaptive environment for data extraction and analysis e.g. in mobile devices [8]. In such solution, traditional legacy systems will require to be imported to the cloud infrastructure and to interoperate in both local and remote clouds. This means that users’ software and APIs will need to communicate successfully and understand the new system constraints.

Based on that, the Service Oriented Architecture (SOA) offers a paradigm to develop software modules for Internet clients’ needs while are provided by the cloud [5]. Using SOA developers could achieve a high level of system granularity by supporting the exchange of information among services as presented in the work of [7]. However, existing services that have been generated from traditional systems are monolithic and difficult to interoperate. This is because of heterogeneous APIs, hypervisors and communication protocols that might use. So, it becomes essential to focus on the integration of solutions that serve as inter-operation strategies for allowing the communication of services, especially services that have been already defined in business processes. This will include the evolvement of the SOA web services to address issues regarding the complexity of heterogeneous services in order to be easily re-used

In this article we focus on the remote monitoring of patients who are hospitalized and monitoring for rehabilitation reasons. The solution proposed is intended to facilitate the work of the caregiving personnel (allows remote monitoring) while improving the quality of life and daily life of patients. The continuous monitoring in an ELE (i.e. in the room of a hospital or in home) will offer significant advantages as the patients are more secure and the staff performs their tasks more efficiently. Also, hospitalization costs reduced significantly as less staff is required.

In particular, elderly or disabled patients, risk injuries during their hospitalization that is provoked by patients’ movements despite the physician’s instructions. A direction could be the continuous monitoring of the patient from the personnel, that is practically impossible. Another case is an emergency situation, in which the patient needs assistance and cannot request it because he/she cannot move. To address such issues, we developed a cloud monitoring system that utilizes a motion sensor device (Microsoft Kinect4) which could be placed in bed or in patient area and interprets patient movements.

The advantages to this solution include the following.

• Increased profits for ELE (i.e. hospitals or physicians) by minimizing the needs to constant monitor patients, thus could serve more patients in an automatized way.

3 http://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf 4 https://developer.microsoft.com/en-us/windows/kinect

• Patients feel safe as the monitoring is continuous and real time data are collected and evaluated by the system, while physicians are notified in case of and emergency.

• Doctors could be informed periodically with regards to the progress of patient’s injuries while they can choose which patient and what feature require to monitor dynamically.

• The system reduced hospital, personnel and maintenance costs for the actual cloud infrastructure. Also, it can promote economic benefits as the application can be used for a time period and bill patients accordingly.

We expect that the proposed architecture will enhance personalization of care management based on the specific characteristics of patients' profiles. It will provide a flexible architecture for analysis of various data from multiple sources and actors, and will allow risk stratification for the specific patient and his/her conditions. It is expected to provide comprehensive and improved therapy treatment coordinated by informal caregivers and based at home environment. Such systems expect to give more advantages to the digital age as the familiarization with the system technology is expected to increase patient autonomy and confidence in complying with the therapy, improve self-management of his/her condition with the help of informal caregivers and reduce the dependency of therapy on the patient actions. As a result, it will reduce the need for patients to organize and attend face-to-face appointments with doctors and might reduce the amount of medication and the number of sick days.

3. Conceptual model

The conceptual model based on a SOA involves different cloud service providers that develop modules, where each follows its own development principles and tools (e.g. operating system, programming languages, natural resources, etc.). Figure 1 demonstrates the service oriented reference architecture for IoT data collections and forwarding to the cloud system based in the work of [3]. The reference architecture represents a model of groups of services that are divided over four main domains, the producers, front-end, back-end and consumers.

Figure 1: The reference architecture for a generic SOA system that includes data collection from IoT devices as presented in [3]

In detail (a) the producers are sensor owners that generate data on an interval, (b) the front-end is a gateway that acts as mediator between the produces and back-end for data exchange, (c) the back-end system includes the general purpose services for user authentication, data context subscription, storage, management of events and the application system (that makes use of standards, controls and conditions for the transfer of information on individual services and orchestrates the business intelligence of the service), (d) the consumers are either end users or other applications that are subscribed to the data. The architecture is based on software modules that operate on the cloud. Below we describe each of the architectures modules.

• IoT Connectivity – Protocol Adapter: The IoT connectivity software module is responsible for connecting the sensor with the FI application components. By using the Protocol Adapter to adapt to the specific connectivity protocol that of the sensor (i.e. Bluetooth)5.

• Sensor Data Collector: It is responsible for collecting the sensor data and then forward it to the cloud. This module is also responsible for converting data into the desired form (i.e. JSON ).

• Connectivity Service: The Connectivity Service establishes a connection between the front-end and the back-end so the data from the Sensor Data Collector can be transferred to be processed by the Application Logic module.

• Complex Event Processing: The Complex Event Processing6 module is used for decision-making thorough the analysis of complex conditional events. The module processes custom event patterns and then, based on specific user defined conditions, decides the flow of the data.

• Cloud Storage: The Cloud Storage module is responsible for storing and retrieving data from a database. Its main functionalities are offered as a REST API because storing and retrieving data should be easily accessed (e.g., by developers).

• Application Logic: This module is application specific. It encapsulates the business logic of the FI application as it handles and processes sensor data. For decision making, the Application Logic is using the Complex Event Processing module and for storing and retrieving sensor data it uses the Cloud Storage module. results to the Publish/Subscribe broker.

• Publish/Subscribe Context Broker: The Publish/Subscribe Context Broker7 receives the results of the sensor data processing from the Application Logic in order to publish them. The role of the Context Broker is to publish context to subscribers. Consider for example a sensor that measures temperature and humidity. Some users are interested in getting updates on the current temperature while others only want to get updates on humidity.

• Identity Management: This module is used for user authentication and access authorization. We utilized the KeyRock authentication service of

5 https://forge.fiware.org/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.IoT.Gateway.DeviceManagement 6 http://catalogue.fiware.org/enablers/complex-event-processing-cep-proactive-technology-online 7 http://catalogue.fiware.org/enablers/publishsubscribe-context-broker-orion-context-broker

FIWARE8. This applies to both users and developers of applications who are based on or have access to services through REST APIs.

4. Motion sensors in ELE using cloud computing

The service centric architecture is based on the idea that any complex problem can be solved optimally and effectively if it is divided into smaller parts. This architecture is comprised by a flexible set of design principles and services that communicate with each other and can be used in multiple, separate systems from several business areas. Some of the advantages that make it particularly well-known include that services are reusable, it allows faster and more efficient debugging, it offers shorter time distribution of new products and applications and services are not bound by the system, but it can be modular. As discussed before, the proposed system involves information producers, including the sensors which produce data and users who interact with them and user interface (front-end) where data collection occurs. Furthermore, as mentioned above, the services-centric divide the problem into smaller other, it is perceived in the part of system administration (back-end) where general purpose services are used so that the system can obtain functionality.

The system is implemented using the Microsoft Kinect that provides us the operations to know the position and movements of users. Specifically, the data provided as a set of points, which make up the human skeleton. These are given the ability to record 20 joints of the human mind-frame (i.e. wrist, knee, etc.) while the frame represents the attitude and position of the user. For each of our points the coordinates are given in three-dimensional form. In particular, the variable X represents the position or displacement user on the horizontal axis X, our variable Y indicating the position on the vertical axis Y and the variable Z the depth to which the user is located from the point where the sensor is located. Next sections define the system description (Section 4.1), the data flow (Section 4.2) and the use cases (Section 4.3).

4.1 System description

The system is designed in accordance to Figure 1, and includes three main sections the user interface (front-end), system management (back end) and the users as follows.

1. User Interface (front-end): This part includes the Microsoft Kinect sensor and the device that is connected to Internet for collecting and decoding the sensor data. The interface allows data forwarding to the cloud at real time. It should be mentioned that a system administrator can insert and remove sensors from the system and save patient information.

2. System Management (Back End): This section consists of general purpose services used for processing and storage of data transported from the Kinect sensor to the Cloud. More specifically, the services are the Publish/Subscribe Orion Context Broker GE and JSON Storage GE, that includes rules for the management of user subscriptions and storing information and data respectively. In addition, this section contains the authentication mechanism of the user entering the application, i.e. KeyRock Identity Management GE.

8 http://catalogue.fiware.org/enablers/identity-management-keyrock

Finally, through the system logic (application logic) the medical personnel can set conditions and rules of the result produced by the application.

Figure 2 demonstrates the system architecture., where the user interface allows sensor instalment, the system management is responsible for the management and processing of data in the cloud as well as for communication between modules and the application logic while the the users are users and application.

Figure 2: The architecture of the Microsoft Kinect sensor service oriented system for data collection from IoT devices in ELE

4.2 Data flow analysis

The data imported from the sensor follow a flow path to the cloud according to a set of rules as follows.

• The Kinect sensor outputs data an interval that we to two seconds. In a case that the user is not in the area of the motion sensor data is not recorded.

• The decoded information is forwarded to the sensor to the Publish / Subscribe Context Broker GE that updates with new data from the sensors.

• The JSON Storage GE receives data from the administrator of the system, the new sensor connection and in what room or space placed and which patient monitor to provide the user with the available rooms that are about to be monitored.

• The users have access to the application with their personal details. The KeyRock service Identity Management GE is responsible for user registration and access.

• The user situated in the application environment has the potential to make a request for assistance in the Context Broker GE or request any patient history and data collected from the JSON storage GE.

• The Context Broker GE service returns after each request for assistance a unique identifier (subscribe ID), so that the system recognizes the room being monitored.

• The application logic module orchestrates the system.

4.3 Use cases

Our system highlights an IoT based solution for the e-health sector in order to assist medical personal to performed their work more easily by taking advantage of modern technologies that enable remote patient monitoring aiming at hospital and physiotherapy centres in the concept of ELE. Next sections present two use cases, firstly a monitor solution for hospitalized patients and secondly a rehabilitation scenario of patients sufferingknee injuries from distance.

4.3.1 Hospitalized patient monitoring

This scenario includes an application of the system motion sensor in the hospital ELE. Initially, a medical personnel i.e. a nursing staff has administration rights and places Kinect sensors specific places in front of patient beds. He/She configure the patient profile per sensor with information i.e. name, hospitalization room etc. In particular, continuous information is provided on whether the patient is in the bed and if some help is needed. By applying the motion sensor solution, we give the ability to monitor many patients from a small number of nurses and doctors. Still, the system facilitates and patients more efficiently as it improves the quality of their nursing experience.

The implementation of this scenario includes installation of the sensor, and characterization of the body parts that produce essential information for being recorded. Figure 3 demonstrates the example case of placing the motion sensor in front of the patient, then the sensor starts monitoring of his/her movements and notifies medical personnel accordingly. The Microsoft Kinect is capable of recording the frame of the human skeleton and tracking his/her actions by recognizing 20 joints in the human body. The sensor placement point is decided based on the sensor configuration, for example Microsoft Kinect operates more accurately when it is at a distance greater than one meter away from the patient and less than two meters. So we concluded that the proper position of the sensor in front of the patient's bed as shown in Figure 3

We monitor the left and right patients’ shoulder for the process in which he/she wants to get of the bed. These two values are required for pre-forecasting the effort to get the patient from either the right or the left side of the bed. We set an upper limit threshold, and if the limit is reached, the application notifies the medical personnel to intervene directly. In the second case, where the patient asks for help, the system identifies the position of the wrist. In the same way will be compared to the limit and once

Figure 3: The scenario of monitoring patients’ movements (i.e. shoulders, wrists etc.) in a hospitalized ELE

that is exceeded, the alert appears as a request for assistance. This notification is submitted to the medical personnel.

4.3.2 Training patients for rehabilitation reasons

The second scenario includes a user suffering a knee injury and he/she is at home for rehabilitation purposes. Such scenario could be to physiotherapy centers. In this case, similar to the previous, the system administrator is responsible to provide the patient with the exercise movements (i.e. what movements to do and what thresholds are acceptable) thus, he/she registers these values to the system. The patient can use the Microsoft Kinect to perform an exercise without being present in the physiotherapy center but in his own space. The doctor after giving appropriate instructions to the patient for the exercise to be performed, can monitor the rehabilitation recovery process remotely based on records regarding the history of the movement and the time incurred. The efficient use of the system will make the transactional aspects of health care more productive by monitoring patient status, activity and compliance with therapy. The proposed model is expected to provide improved therapy treatment coordinated by informal caregivers and based at home environment.

As in first scenario, we exploited the dynamic recording of the skeleton by tracking information for recognizing the movement. In more detail, the sensor is placed in a distance of over 1 meter in order to receive more accurate results as shown in Figure 4. The selected values for this case is the position of the injured ankle. Initially, the user requires to have the leg in the ground and the exercise includes a check of the position that is translated to the height (that has been set by the physician). According to this position, the user is informed for the maximum exercise height at which the

patient was able to move the injured leg. To conclude, the familiarization with the system technology is expected to increase patient autonomy and confidence in complying with the therapy, improve self-management of his/her condition with the help of informal caregivers and reduce the dependency of therapy on the patient actions. The system at a further step has been designed by considering multi-channel information on the specific patient’s condition form formal and informal caregivers and are expected to encompass a holistic view of his/her health status.

5. Conclusions

This article presented a patient monitoring solution in an ELE where users can utilize several devices to provide personalized information regarding the temporal health status and reaction to the given therapy. The informal caregivers can also provide information regarding the achievement of therapeutic goals. Our work promotes ELE as the wider community can be seen as a means of empowerment and encouragement for the patient to work towards improving

Figure 4: The scenario of monitoring a patient in his/her home ELE by developing exercises for knee injury rehabilitation

his/her condition. We demonstrated an IoT reference architecture that represents a model of groups of services that are divided over four main domains including the producers, the front-end, the back-end and the consumers. These are usually deployed as modular cloud services that may belong to heterogeneous cloud providers.

The use of the system will help in implementing the secondary prevention measures in the transition from hospital to ambulatory and home care ensuring patient compliance with the therapy and re-assure its effectiveness. The personalized recommendations that the system supports are produced after careful consideration of multi-channel information on the specific patient’s condition. Thus, the proposed solution is expected to provide comprehensive and improved therapy treatment coordinated by informal caregivers and based at home environment. We also designed the use case system to be based on user friendly interfaces for the elderly and people that are not familiarized with technology so that it become well accepted by such user groups. Moreover, the familiarization with the system technology is expected to increase patient autonomy and confidence in complying with the therapy, improve self-management of his/her condition with the help of informal caregivers and reduce the dependency of therapy on the patient actions.

We aimed to achieve a quality of the health care services provided and simultaneously help to reduce the cost of healthcare, patients spend less time in the hospital and yet have more detailed health data. We expect that this will allow caregivers to react more quickly to the medical emergencies of elders; and patients can self-manage their own health and wellness in ELE. An important part of the system proved to be the use of general purpose services that integrate its functionality. As the cloud technology advances, more and more applications will be carried-out as it offers adequate space and appropriate tools to the build intelligent systems. The system has the ability to appropriate future expansion to become more functional and serve the daily needs of people. As future development we aim to include dynamic addition of new motion sensors, inclusion of sensors such as heart rate, pulse etc. in order to allow more sophisticated patient monitoring. We also aim to explore different aspects of the system performance related with network delays and accuracy of sensor data collection with regards to high bandwidth data flows.

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