. This will result in US$ 1.9 trillion in global economic value-added through sales in diverse end markets (Gartner, 2013). Many researchers have been active in healthcare restructuring that leverages IoT technologies in medical asset management (Lee & Pala‐ niappan, 2014; Ng et al., 2014), optimizing medical resources (Jara, 2014; Xu et al., 2014a; Jara et al., 2010), monitoring healthcare situations (Shahamabadi et al., 2014; Sung & Chang, 2013; Castellani et al., 2012; Istepanaian & Zhang, 2012; Luo et al., 2012; Sung & Chiang, 2012; Istepanian et al., 2011), and increasing the use of home healthcare (Monares et al., 2014; Sebestyen et al., 2014; Yang et al., 2014; Yang et al., 2014a; Pang et al., 2013; Tarouco et al., 2012). Lee and Palaniappan (2014) developed an RFID- based inventory management system (RFID-IMS) for tracking medical devices’ utilization and managing their inventory levels using real-time data. Jara (2014) conducted a feasibility study for the use of RFID/NFC (Near Field Communication) technologies for improvement of quality assurance in drug identification. Shahamabadi et al. (2014) proposed a solution to establish a hospital wireless network (i.e. mobile network) using 6LoWPAN technology for healthcare monitoring in the IoT environment. Xu et al. (2014a) demonstrated a resource-based data model to store and access the IoT data to support decision-making in emergency medical services.
Logistic domanin which is managed by FM, according UNI EN 15221, appears to be invested as well by the IoT application, thanks to its quick processing technology based on RFID and NFC which allow to control supplychain, ranging from commodity design, raw material purchasing, production, transportation, storage, distribution and sale of semi-products and products, returns’ processing and after-sales service.
Moreover it is possible to collect products’information, promptly, timely, and accurately so that enterprises or even the whole supply chain can respond to changeable markets in the shortest time, as performed by Walmart and Metro \citep{Rao_2009}.
Intelligent informatics system (iDrive system) developed by BMW used various sensors to monitor environment in order to provide driving directions for drivers [59]. Other operations during transportation and logistics, such as routes control, certain warning emission on container storing, can be enhanced by IoT as well [77].
[59] E. Qin, Y. Long, C. Zhang, L. Huang, Cloud computing and the internet of things: Technology innovation in automobile service, Human Interface and the Management of Information. Information and Interaction for Health, Safety, Mobility and Complex Environments, Springer, Berlin Heidelberg, 2013, pp. 173–180.
[77] O. Vernesan, P. Friess, G. Woysch, P. Guillemin, S. Gusmeroli, et al., Europe’s IoT strategic research agenda 2012, The Internet of Things 2012, New Horizons, Halifax UK, 2012, pp. 22–23.
Furthermore, energy management which is one of facility manager’s major issues is faced off through IoT integration, by avoiding energy waste and optimizing product life-cycle energy management. As data analysis is one of the most advanced technique in FM, thanks to its new calculation models (static attribute data acquisition, dynamic data acquisition and fuzzy environmental information perception)(*), data acquisition layer is crucial in gathering energy information. With IoT technology all components, machines and facilities in product life cycle can be all supported by IoT end-nodes, making possible to acquire more accurate energy parameters from item’s status at any time.
(*)Internet of Things in product life-cycle energy management (Fei Taoa,∗, Yiwen Wangb, Ying Zuoa, Haidong Yangc, Meng Zhanga)
Microsoft Corporation, one of the biggest global player in software field, are exploiting IoT application with its IoT platform Azure, through which many companies are taking advantages. Microsoft Azure is establishing itself as a public cloud platform of choice for industrial IoT solutions and Predictive Maintenance. According market report, more and more applications of predictive maintenance are shifting from on-premise to cloud setups – by 2022 about 70% of predictive maintenance setups are expected to be cloud-hosted. Besides the cloud infrastructure, Microsoft Azure currently has two
“preconfigured solutions” which provide quick analytics engines: “predictive maintenance” and “remote monitoring”.
Data dimension, which a “thing” can produce in few seconds, represent an important issue. For instance, a single autonomous car could generate as much as 100 GB of data every second. (da conferenza IoT Microsoft). Simply collecting data doesn’t bring added value if these data aren’t properly processed. Mitsubishi and Vestas have been involved in a projectcalled Burbo Bank Extension, for IoT Wind Turbines deployment, by using Microsoft platform, in which more than 30.000 data collection per second were collected, producing more than 11.000 Gb Data per year, equivalent to more than 1 Billion e-mails, 60.500 hours of movie streaming or 2.750.00 hours of music streaming. 100 sensors here have been installed in each blade.
Security here arises from the endpoint, through the connection, to data, applications, and the cloud. Speed is performed in preconfigured solutions for the most common IoT scenarios. Openess represents the easily connection of any device, OS, data source, software, or service. Scalability allows the platform to grow effortlessly with millions of devices, terabytes of data, on-premises, in the cloud, in the most regions worldwide.
So far Microsoft Corporation has created Microsoft IoT Central, an IoT SaaS (software-as-a-service) solution that makes it easy to connect, monitor and manage IoT assets. Microsoft IoT Central lowers the barriers of entry for companies looking to revolutionize their business with IoT.
Thyssenkrupp, for instance, has created a strong connection among its asset and Microsoft platform, by creating MAX system, to perform a cloud-based predictive maintenance solution. Thyssenkrupp performs an IoT predictive maintenance by gather data from sensors and systems to create valuable business intelligence. This ha reduced elevator downtime by 50%. Applying the Internet of Things (IoT) to elevator maintenance, experts from thyssenkrupp and Microsoft spent two years developing MAX, the industry’s first real-time, cloud-based predictive maintenance solution. MAX leverages the power of Microsoft Azure in order to create predictive maintenance service with the power to maximize elevator uptime. MAX works according this scheme: Data collected, Precise diagnoses, Predictive intervention. Machine data, such as door movements, trips, power-ups, car calls, error codes, etc., is collected from MAX- connected elevators worldwide. This data is sent to the cloud where unique algorithms analyze it for patterns and compute the equipment’s operation and the remaining lifetime of components. Precise and predictive diagnostics are delivered to the technician in real time, indicating where intervention is required.
Moving from reactive troubleshooting to proactively preventing failures, MAX provides advance information about the wear and tear of elevator components, allowing to plan future costs and schedule disruptions.
Maintenance implementation through IoT application
This paragraph aims to investigate how it is possible to take innovation in maintenance among FM sector, by the exploration of current maintenance’s best practices inside industrial and manufacturing field.
Scientific literature shows a wide view on predictive maintenance’s case performed over industrial equipments and machineries. Whereas little studies are highlited on service equipments’proactive maintenance, which represent a domain oversees by facility management.
Many maintenance modalities to be performed over components exist today, but there is no a general rule to carry out a particular maintenance practice, as these depend on the particular and changing problem faced off by facility manager. Nevertheless predictive maintenance appears as the one which is most suiteble to IoT thinking, together with condition-based maintenance, as it is founded on the same principles of dynamism, analyticity, efficiency and foresight. UNI EN 13306:2010, preventive maintenance is an operation “carried out at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or the degradation of the functioning of an item”.
Application of IoT concept on predictive maintenance of industrial equipment
Current challenges in union between IoT technology and manufacturing systems is represented by communication between standard industrial equipments and platforms.
Industrial equioments use several serial protocols to communicate one with another by only sending one bit at a time. This is slower than parallel communication standards, but it can be used over longer distances.
Before the widespread of IoT, several techniques have been deployed in industry for maintenance analysis of industrial equipments. Condition-Based Maintenance has been a wide used merthod which consists of three main steps: data acquisition, data processing and maintenance decision-making. It is based on a maintenance program which implies maintenance decisions founded on the information collected through condition monitoring [2].
Nowadays in the manufacturing practices predictive manintenance has changed the role of maintenance function within industrial needs. In this context, new techniques for maintenance are proposed [4], basing on the fault prediction and IoT system [5].
Predictive maintenance with IoT implementation is addressed into the collection and analysis of data coming from assets, in order to provide the following benefits:
1) Identifying key predictors to determine likelihood of outcomes;
2) Optimizing decision-making by systematically applying measurable real-time and historical data;
3) Planning, budgeting and scheduling maintenance repairs;
In this sense, predictive Maintenance consists in continuously detecting the most significant machine or service equipments parameters, such as oscillations or temperatures, and in dealing with them appropriately through appropriate algorithms. The advantage of this method, linked to IoT, is the ability to acquire quantities that are difficult to measure or can not be measured directly. For instance, the presence of peaks in a spectral diagram can signal the presence of vibrations, which suggest that bearings have worn down. More than FM applications, Data science techniques are necessary for performing an effective proactive maintenace through Big Data elaboration. From data science application in proactive maintenance may occur different analysis. According SAP, which has developed a document titled “Data Science and Machine Learning in Internet of Things anche Predictive Maintenance”, 5 categories of analysis typologies can be listed: trend, relation, segmentation, association and anomaly.
In trend category data behaviour over time is analyzed, in order to detect any process out of control and to estimate component lifetime. Relation category allows to research failure causes of a machinery, by conducting a fault tree analysis. Segmentation category indicates the possibility to cluster or clearly split data. In association category correlation among data are made in order to detect if a deterioration in performance of machinery and a breakdown are linked. Anomaly category shows which values result as unusual, to understand if they are errors or effective changes of a service equipments.
Predictive maintenance practices are associated with little data, because of their rarity, that are difficult to historicize. Here data analyst’s competence allows to include these variable in a predictive analysis process.
According to a recent survey report conducted by the Aberdeen Group, “Best-in-Class” companies are increasingly utilizing IoT and Big Data to implement Predictive Maintenance models to address and improve their operational challenges:
1) Reduce unplanned downtime to 3.5% – Amount of unscheduled downtime against total availability;
2) Improve Overall Equipment Effectiveness to 89% , calculated as: Availability x Performance x Quality = OEE;
3) Reduce maintenance costs by 13%– Total maintenance costs including time and personnel;
4) Increase return on assets by 24% – Profit earned from equipment resources through improved uptime;
Although industrial maintenance reveals conceptual differences from FM’s maintenance, it appears clear that their targets, that are machinery and plant systems, show the same fundamental operations: “work accomplishment powered by energy that, through a given process, is transformed by the system into another form, by exploiting a certain force and developing a certain power”. Then both industrial and service equipments, when a maintenance manual is set up, display same failure variables, operative conditions and intervention’s frequency. Morover communication protocols, architecture layers, hardware, software and middleware parts, gateways and end-nodes (sensors and actuators) have the same functionality in a general IoT’s perspective, both for service and industrial equipments. Infact technologies, which seem to vary drastically, are mainly based on the same power source and devices’ functionality. For example, a consumer machine such as an elevator uses different data transmission protocols comparing to the sensor units in an environmental sensor network. But differences occurs here just on IoT architecture’s levels, rather than in a specific application field, such as service and industrial. The former may transfer data into the Cloud via WiFi directly, whereas the latter in general uses RFID for network communication, which sents stored data in a staging data node before it can be transmitted into the Cloud. (From Predictive Maintenance: Machine Condition Real-Time Monitoring and Failure Prediction, World Academy of Science, Engineering and Technology International Journal of Mathematical and Computational Sciences Vol:10, No:5, 2016, Authors : Yan Zhang) Dissimilarities are shown here just in level crossing among IoT architecture.
Last but not least, foresight techniques such as data mining, machine learning (artificial neural network, fuzzy logic) and text mining allow to strengthen maintenance and IoT paradigms, through parameters’forecast such as trend cycle, irregularity and data extrapolation, in order to create a systematic model. A forecasting model is that the activities responsible for influencing the past will continue to influence the future. (Chase, 2013), as in maintenance practice occurs. ([7] Chase, C. W. (2013), Demand-Driven Forecasting: A Structured Approach to Forecasting, Wiley). The use of numerical methods, implemented through a predictive maintenance’s software, guarantees to the user the ability to detect and evaluate data acquired by the machine. In this way the user has access to a wide range of ready-to-use features (i.e. for frequency analysis) while maintaining full flexibility in the implementation of their algorithms. Currently, analytics-driven predictive maintenance is achieving attention in many industries such as manufacturing, utilities, aerospace, etc., thanks to the expansion of IoT applications and the maturity of technologies that support storage and processing of Big Data.
Literature presents proactive maintenance’s cases aim to analyze health status of industrial equipments. The most innovative nowadays trends show the creation of maintenance activities performed from analytics solutions that include both real-time machine condition monitoring and machine learning based predictive analytics capabilities. These allow to carry out: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure.
(From Predictive Maintenance: Machine Condition Real-Time Monitoring and Failure Prediction, World Academy of Science, Engineering and Technology International Journal of Mathematical and Computational Sciences Vol:10, No:5, 2016, Authors : Yan Zhang)
In “IoT Company Ranking”, survey carried in 2016 by IoT Analytics, market insights for the internet of things, a list of active companies involved in predictive maintenance with IoT is shown. The list is compiled starting from three indicators: monthly searches on Google in conjunction ith “predictive maintenance”, 2016 newspaper and blog mentions in conjunction with “predictive maintenance” and number of employees that carry the tag “predictive maintenance” on LinkedIN in January 2017.
Here IBM is presented as the major world predictive maintenance’s practioner. IBM Predictive Maintenance and Quality, a software solution, is one of the key solutions enabled by its “cognitive intelligence engine” IBM Watson. It monitors and reports on equipment data, gathering from assets. Known examples for predictive maintenance implementations are Kone’s elevators or DC Water’s Hydrants. Kone recently launched its 24/7 Connected Services, based on the IBM solution.
Sap, a german software company, represents the second major firm which carries out predictive maintenance trought IoT application. SAP has implemented its solution “Predictive Maintenance and Service”, through SAP Leonardo IoT Portfolio, for customers such as Kaeser Kompressoren or Siemens.
Other industrial and service provider companies such as GE, General Electric, performs predictive maintenance through two moments: measurements and asset management. The former is managed through GE Digital, which covers the software and analytics part of predictive maintenance, by establishing the company in the condition monitoring hardware field. Whereas the latter is represented by GE’s Predix platform to control. GE has, for instance, rolled out APM with
BPs oil and gas production operations. Furthermore, GE Digital is advancing the concept of digital twin, an important basis to Predictive Maintenance analytics.
Yang et al. [5] created an Internet-based remote maintenance system for managing processes. Feldmann and Göhringer [6] implemented an Internet-based diagnosis for a maintenance’s monitoring system. Lung et al. [7] created toolbox to promote decision making over maintenance actions. Mori et al. [9] studied the operation’s status of 8000 machine tools simultaneously worldwide to improve their maintenance’s efficiency. With respect to IoT maintenance systems, Yonggang et al. [10] created a boiler remote monitoring system with IoT cloud platform’s applications. Wang et al. [11] developed an IoT application for fault diagnosis and prediction, based on machine learning. Alexandru et al. [14] created a smart web-based maintenance system for a more automated manufacturing environment.
[5] S.H. Yang, Ch. Dai, and R.P. Knott (2007) “Remote Maintenance of Control System Performance over the Internet” Control Engineering Practice, 15(5): 533-544.
[6] K. Feldmann, J. Gohringer (2001) “Internet based Diagnosis of Assembly Systems” Annals of the CIRP, 50 (1):5–8.
[7] B. Lung, M. Veron, M.C. Suhner, A. Muller (2006) “Integration of Maintenance Strategies into Prognosis Process to Decision-Making Aid on System Operation” Annals of the CIRP, 54(1):5–8.
[9] M. Mori, M. Fujishima, M. Komatsu, B. Zhao, Y. Liu (2008) “Development of remote monitoring and maintenance system for machine tools” Annals of the CIRP, 57:433–436.
[10] Yonggang Gong, Aide Zhou, Yanan Xiao, (2014) “Design of wall- mounted Boiler Remote Monitoring and Control System based on the Ayla IOT Cloud Platform” Applied Mechanics & Materials, Issue 571-572: 1047.
[11] Chen Wang, Hoang Tam Vo, Peng Ni, (2015) “An IoT Application for Fault Diagnosis and Prediction” IEEE International Conference on Data Science and Data Intensive Systems: 726-731.
[14] Ana M. Alexandru, Alice De Mauro, Maurizio Fiasché, Francesco G. Sisca, Marco Taisch, Luca Fasanotti and Piergiorgio Grasseni, (2015) “A Smart web-based Maintenance System for a smart manufacturing environment” IEEE 1st International Forum : 579-584.
Based on AEI and RFID, IoT technologies have been widely used in the train dispatching and labor jobs management in GuangZhou EMU maintenance base. Technological solutions for the production-line and spare parts delivery have experienced pilot test with corresponding software like projects scheduling, process control, working processes monitoring, logistics management, etc. Engineering practice showed that IoT technologies benefit the efficiency upgrading of maintenance jobs flow control, the reducing of labor intensity and failure probability, especially the richness of productive information. Further steps will be made for attaining mature application through pilot test and technological solution improvement, while realizing comprehensive utilization of the real-time multi-source productive data of EMU base, establishing an intelligent architecture for EMU maintenance application. (Weijiao ZHANG)
Still on rail transport sector, Ansaldo STS, a society inside Hitachi Group Company, specialized in railway signalling and integrated transport systems for mass transit and freight rail operations, is performing an interesting IoT application in predictive maintenance and asset management. IoT for this company is an instrument to create value in service provisions. Value in services is here reached when data are collected and storage in an informative system which elaborates them into informations. The availability of new data is expected to bring benefits in multiple company’s field such as: smart ticketing and intermodal mobility, human flow, passenger information and on board entertainment systems, security. IoT in Ansaldo STS allows to reach integrated rail operations by integrating information about intelligent traffic management system embedding rolling stock and crew management. Morover Iot application enables cost reduction by collecting real time information about asset status, by promoting predictive maintenance’s strategy.
Actually asset management, inside FM field, oversees IoT’s wide applications. Here data collected from trains and railways are historicised and presented to the client, to analyze equipments’performance, nominal working conditions and behaviour’s critical factors. This kind of informations permit to foresee failure rate. Ansaldo STS is passing from a preventive maintenance, based on dated surveys and number of operations performed, to a predictive maintenance based on big data. The scheduling of activities and the ticket activations is managed by the information system connected to the cloud. This concept is applied as well to IoT inventory management level: the group automatically orders the spare parts. IoT application, in Ansaldo’s experience, acts also on technical services of trains (air conditioning system, electrical system and security system) which sends their data to a on board database. Depending on their alertness grade, data are elaborated inside cloud, when train can connect to the first useful station’s network to undertake operations (mobile protocol). Moreover mos railways have equipped special trains (rowing fleet) with on board diagnostic equipment that run on the network to collect data regarding track, geometry, catenary status, etc. Rete Ferroviaria Italiana (RFI) has a fleet of diagnostic trains (Archimede, Talete, Aldebaran, Galileo e Caronte 1 e 2) for Conventional lines and High Speed Lines (Diamante) and is now planning to spend 65M Euro to upgrade this fleet. Whereas Société Nationale des Chemins de fer Français (SNCF) has a fleet of diagnostic trains that runs on the network and every two weeks is able to survey the entire High Speed Network.
However this kind of applications can face many problems. First of all, costs linked with data collection of obsolete trains is a relevant issue, as sometime assets and collecting data nertwork haven’t the ability to exchange informartion. In this way a deep analysis has to be undertaken to undestand the convenience to install sensors or to substitute completely the assets. Then data must arrive with a certain reliability and a certain constancy to the cloud.
Bosch group is investing in IoT systems through its 4 main divisions: mobility solutions, consumer goods, industrial technology and energy and building technology. This transformation involves the Bosch thinking to track goods’performance from manufacturing phase to end-user phase. Bosch produces sensors (end-nodes in IoT perspective) which represents the automotive basis, in which the group is a global player. Bosch sensors with Bosch IoT cloud allow to develop a scalable and flexible IoT, which is implemented through software and data analytics.
Moreover Bosch’s ioT practice achieves service offer, which include training and counseling to integrate partners and customers in I”oT ecosystem”.
Bosch IoT strategy contemplates “flexibility”, as a main IoT feature, achieved thanks a distributed intelligence. This is possible for the deployment of the edge computing’s application, in which calculations are not only carried by the central cloud. Edge computing allows to enlarge the IoT perspective beyond Bosch company’s boundaries, by comprising clients, suppliers and final users. Value’s production network becomes more and more pervasive, thanks to the IoT’s adoption. This enable to follow life cycle’s steps of all techinical equipments and components and permit to track the company workers’skill in a digital way, by collecting data and depicting realtime data. This is reached thanks to the “open standards” adopted by Bosch group, which promotes the interoperability in technologies’communication. Open standards, inside Bosch group are adopted in order to enlarge the partnership inside the company.
In Powertrain Solution, the biggest division inside Bosch company, it is pursued the concept of flexible integration of machines. Here more than 5000 sensor-provided-machines can exchange knowledge. If a failure occurs inside a industrial plant, a knowledge management system can detect a break down and suggest, without any human intervention, a solution. This concept is linked to system’s reconfigurability, based on the distribuited intelligence.
So supply-chain has been integrated in Bosch network. Digital life-cycle management, moreover, allos to comprehend when a particular product is becoming obsolete or when withdraw a products class to promote another one, thanks to advacenced data analysis’s methods. In the informative system a product’s hystory is uploaded, by representing it through a virtual real-time representation: each product is equipped with a sensor which tracks its quality and its production chain.
A case study (Parpala et al.,2017) on predictive maintenance comes from the manufacturing field. Here maintenance practice is implemented thanks to the platform deployment which control an polishing and sanding machine for high gloss lacquered furniture components (painted MDF – medium-density fibber).
The machine can perform three movements: the motion of the conveyor table, with the fixed wood panel, the motion of the brush head in a transversal direction to the wood panel and continuous rotation of the brush. These three movements correspond to 3 communication standards on moto drivers. Data related to speed, functioning time of motors, working status of the motors and the temperature near the main motor are collected and sent to an open IoT platform, named Carriots. Hardware part (Arduino Uno board) is connected between the equipment and the IoT platform. If a Start or a Stop command is received, based on the protocol, IoT platform will receive the data. Arduino device is here chosen because it packs considerable power on a very small board and it provides many opportunities for automation, networking and data collection, even if Arduino is generally not so used in industrial application because of its low level voltage, robustness and safety.
Developing a predictive maintenance strategy means here to send usage alert via e-mail or other messaging services, basing on the achievement of temperature of 27 °C. Each time a defined threshold is reached, an e-mail alert is send to the user. So far other parameters can be added and different alerts can be defined. In this way on-line monitoring and predictive maintenance system can be performed for any type of industrial equipment.
RESEARCH QUESTIONS
The object of the research arises from four research questions:
1) What is the possible healthcare management model for the IoT techniques and sensor equipment’s support?
2) What are the most suitable data management methods?
3) What are the parameters to be detected?
4) How to reconfigure organizational management models?
Starting from these research questions the following objectives are met:
1) Exploration of IoT’s potential inside facility management and discussing core technologies that can reshape healthcare technologies based on the IoT;
2) Research of IoT-based healthcare components to be managed and the definition of the parameters useful to control process;
3)Definition of theoretical models with IoT system approach suitable for
management of maintenance equipment within hospital infrastructure;
4) Creation of a set of rules, covering all probable requests of typical hospital facilities;
5) Individuation of sensors and other devices to detect efficiency information or diagnosis at every stage;
6) Definition of general suitable platform useful for managing data for maintenance management of buildings;
7) Detecting and preventing systems failures or uses;
METHODS AND TECHINIQUES
Having regard to the proposed objectives, the research project intends to adopt the following working methodology:
1) Deep literature review and case-study analysis at the international scale of the current practice of healthcare buildings FM, focusing on maintenance planning and programming techniques;
2) Comparison between maintenance management models for technical equipments in buildings and relationships between different supply-chain operators;
3) Analyzing the theoretical and regulatory structure inside best practices and processes for healthcare facility management;
4) Monitoring of existing hospital facilities and their performance in order to the definition of requirements for data gathering, dynamic maintenance planning of plants;
5) Defining parameters (Input, output, supportive), indexes and rules to evaluate over time systems healthcare facility performance;
6) Proposal of innovative models for management of technical facility for IoT applications;
7) Definition of sensors apparatus;
8) Hypothesis of Iot systems case studies BIM Application;
9) Hypothesis of new organizational models of services: Analyses of FM structures organizational models in relation with their changes with regard on contracts, construction permits, project management and construction management;
9) Analysis of IoT applications which gather datafrom sensors with power, robustness, durability, accuracy and reliability;
10) Use of BIM systems for modelling and monitoring the information flow of hospital facilities;
EXPECTED OUTCOMES
By responding to the previous objectives, it is expecting from this research to reach the following outcomes:
1)Definition of the characteristics of an advanced IoT-based support system with more stringent requirements and with run-time libraries, for a service-oriented approach (SOA);
2) Maximize asset visibility, utilization and performance while better managing regulatory compliance efforts. Increase hospital equipments availability, help reduce acquisition, operating and maintenance costs, and improve return on assets;
3) Creation of an innovative and unique IoT-BSMS (building service
management systems) which gathers the potential of IoT technologies and artificial based intelligence (neural networks, fuzzy logic, genetic algorithms) with best practices management system;
4) Definition of parameters necessary for healthcare’s management and its sensors of detection;
5) Creation of few FM models which adopt IoT and which are expected to reduce costs, improve the effectiveness of management processes and increase the reliability of equipment;
7) Creation of rules, which combine input and output parameters for
decision support unit;
8) Identifying of correctly IoT optimum times for replenishing supplies for various devices for their smooth and continuous operation by the creation of items based on the logic of Sensing and responding, sensing and knowing, sensing and learning;
9) Definition of the characteristics of an advanced IoT-based support system with more stringent requirements and with run-time libraries, for a service-oriented approach (SOA)