U6 Flashcards
In general, IIoT primarily focuses on ?
business scenarios integrating vertically (i.e., from machines to cloud), horizontally (i.e., among supply networks), or along the life cycle of the product.
There exist some significant challenges that are unique to the IIoT architecture implementation, including:
(1) achieving productivity gains in terms of higher throughput and efficiency and eliminating non-value-adding activities;
(2) failure prevention and poor product quality;
and (3) flexible design through hiding complexity, low configuration, or reconfiguration effort, plug and produce, and avoiding technology gaps.
IIoT represents a special case of the general IoT reference architecture so that the operational technology (OT) layer and the IT layer in a manufacturing context can?
be easily integrated.
The IIoT reference architecture consists of three main layers:
edge, plant, and cloud/enterprise.
Before delving into the IIoT reference architecture, it is worth mentioning that the three-layer approach emerges from the need for ?
the individual factory to continue operation even if external connections to enterprise and cloud systems should fail.
In other words, stopping the production lines for an external connection failure is unacceptable.
In addition to IIoT, the three-layer approach is also common in other IoT scenarios such as:
smart buildings, where a local entity must continue operating smoothly even if connectivity to centralized IT systems fails.
The above figure demonstrates a scenario from automotive manufacturing for?
monitor production equipment and tools for various performance metrics.
In this scenario, analytics is to be performed both at the edge (applying the emerging edge analytics architecture) on?
edge devices and at the enterprise layer.
At the edge, the equipment associated with production operations is to be continuously monitored by ?
various sensors.
Examples of these equipment include :
production machines, smart devices, robots used for welding, and handling equipment, e.g., conveyors, palletizers.
Such devices and production machines are typically managed by SCADA systems, which can be integrated by industry protocols such as:
Profibus,
OPC,
and MODBUS.
Some newer equipment is embedding technology that allows it to?
communicate with the outside world through IT protocols such as MQTT.
The sensory data from intelligent devices and production machines can be communicated up through?
the layers with appropriate filtering and aggregation along the way.
As it can be seen above, gateways are typically utilized to integrate with?
the existing systems and equipment.
The gateways also become more capable of ?
running edge analytics,
applying rules,
and even storing data locally to support operations at the edge.
Accordingly, the edge completely handles an interaction with equipment with no involvement of?
the plant or enterprise layers.
In other scenarios, the sensory data may flow up from the edge through the plant or to the enterprise where?
plant and enterprise analytics is performed in a similar way.
To account for possible connection-failures, the edge and plant need to be able to ?
operate as a stand-alone unit from the enterprise,
therefore some capabilities of the platform need to be in both the plant and the enterprise.
In the automotive scenario, sensory data is collected from the equipment and tools initially by?
programmable controllers connected to the equipment through proprietary equipment interfaces.
The controllers can be configured to pass the sensory data to the?
upper layers of the architecture via standard IT protocols like MQTT.
Such data transfer can be performed periodically or ?
based on conditions.
The data can also be transformed into different formats as?
needed before it is passed on to the subsequent layer.
Edge Analytics is typically performed on?
the outbound information in the OT/IT hub.
Depending on the result of the Edge Analytics, command data is?
sent back down to the equipment.
This reverse flow into the edge issues the command and transform it into?
the specific protocol and data needed by the equipment.
A Broker component of the Edge controller forwards events, based on?
configuration, to the Plant Service Bus, e.g., IBM Watson IoT Platform.
In some cases, plant data is not allowed to leave the premises for?
security reasons.
Operational data is collected at?
the plant level after normalizing and cleansing to support plant-level analytics.
An information model, based on the ISA-95 industry standard, is utilized to?
support the analytics and is also used for dashboards and reporting as well.
Within the Plant Service Bus, analytics and rules determine?
the required actions for each event.
The required actions can be in the form of feedback, but they can also?
include triggering actions represented in a workflow.
In general, Data Analytics can be simple in the form of threshold monitoring or trending, but it could also be?
model-based analytics, looking at the performance of a production device, a tool, or a production process.
In other situations, additional tools can be incorporated such as:
predictive maintenance,
predictive quality,
and plant performance analytics.
Based on the result of the analytics and rules, information may flow back to the Edge and to the production equipment. Such information may result in?
dynamic reconfiguration of the manufacturing process.
In general, smart agriculture applications require?
the large-scale collection and processing of sensor-derived data for optimizing crop management.
To this end, appropriate reports are created and offered to bio-plant experts, which accordingly can?
interpret the measure for crop optimization.
In particular, the OpenIoT platform employs the SSN ontology as?
a standards-based model for the semantic unification of diverse IoT systems and data streams.
In addition, OpenIoT exploits cloud-computing concepts, e.g.,?
on-demand, utility-based, pay-as-you-go access to resource with sensing/IoT concepts.
Thanks to these features, several organizations (notably OpenIoT partners) have successfully built and deployed applications based on?
OpenIoT, while other organizations and projects evaluate the platform for their own purposes.
Before delving into the smart agriculture application, we introduce?
the main components and data flow in the OpenIoT architecture.
As depicted below, the OpenIoT platform consists of ?
seven main elements that belong to three different logical planes.
Such planes are:
the utility/application plane,
the virtualized plane,
and the physical plane.
In the utility/application plane, there exit three components:
request definition,
request presentation,
and configuration and monitoring.
The request definition component is used to create service requests to?
the OpenIoT platform through a Web 2.0 interface.
Specifically, it comprises a set of services for specifying and formulating such requests, while also?
submitting them to the global scheduler.
The request presentation component is used to?
facilitate a service presentation in a Web 2.0 interface.
To visualize these services, the request presentation component communicates directly with?
the service delivery and utility manager to retrieve the relevant data.
The third component enables the management and configuration of ?
functionalities over the sensors.
Moreover, it enables users to monitor the health of the different deployed modules.
In the virtualized plane, there are also three components:
scheduler,
cloud data storage,
and service delivery and utility manager (SDUM).
The scheduler processes all requests from?
the request definition and ensures their proper access to the required resources, e.g., sensor data streams.
First, it discovers ?
the sensors and the associated data streams that can contribute.
Afterward, it manages a service and selects/enables the resources involved in?
service provision.
The second component, referred to as linked stream middleware light (LSM-Light), is used to?
store the data streams generated from the sensor middleware, thus acting as a cloud database.
The third component combines the data streams, as indicated by ?
service workflows, to deliver the requested service to the request presentation.
To this end, SDUM utilizes the service description and resources identified and reserved by?
the Scheduler component.
Additionally, SDUM acts as a service metering facility, which keeps track of ?
utility metrics for each individual service.
In the Physical plane, the Sensor Middleware, referred to as?
the Extended Global Sensor Network (X-GSN) collects,
filters,
combines,
and semantically annotates data streams from virtual sensors or physical devices.
It acts as a hub between ?
the OpenIoT platform and the physical world.
Based on the architecture demonstrated above, the data flow within?
OpenIoT systems is as follows.
At the outset, X-GSN nodes announce the available sensors to a directory service, before?
starting to publish their data in SSN-compliant RDF format based on each X-GSN local configuration.
A user typically sends a request to?
the scheduler component to retrieve all the available sensor types that satisfy specific attributes.
To fulfill the user request, the scheduler sends the directory service a request to?
retrieve the available sensor types.
The reply is forwarded to the request definition UI from ?
the scheduler and the retrieved information is prov ided to the user.
CSIRO
This is the Commonwealth Scientific and Industrial Research Organization in Australia.
In agriculture, analyzing the size, growth, and performance of plants in a greenhouse or field site can be?
a time-consuming and expensive task.
Therefore, the main objective of smart agriculture systems is to?
collect this information from remote locations and send it back to the laboratory in real time.
In this realm, CSIRO has initiated the Phenonet project in which an autonomous WSN is deployed to?
record environmental conditions and wirelessly transfer data to a data store.
The collected data is then used to evaluate the effect of sheep grazing on?
crop re-growth through looking at root activity, water use, crop growth rate, and crop yield.
The experiment tries to compare trade-offs between?
grazed and un-grazed setup for a particular variety of crop.
This experiment is supposed to last for nine months. The animals come in the first six months and after that they are removed from the site.
To compute the root activity and water usage, a soil moisture sensor is———–.
deployed
Sensors at one depth level do not tell the entire story. Multiple depths are needed in order to?
observe behavior of the root system and the water available to the crop at any particular time.
The target functionalities include:
(1) efficiently and effectively managing the water resource;
(2) efficiently and effectively administering the timing of using fertilizers;
and finally (3) increasing crop yield by efficiently utilizing the resources (water and fertilizer) while allowing livestock to feed on healthy crop leaves.
The phenonet architecture has primarily five high-level stages, i.e.,?
field,
data store,
data analysis,
visualization,
and end user. Specifically, the field is an area comprising different types of crops varieties.
The WSN is deployed in the field to measure various environmental features such as :
soil temperature, crop canopy temperature, humidity, and wind speed.
Through collecting this data, the crops’ growth, performance, and size can be?
continuous computed in real time.
The second stage of the system involves:
storing all captured data in a safe location.
At the storage state, both sensor measurements and metadata information, e.g.,?
sensor types, serial numbers, MAC address, experimental treatment, crop sowing date, are to be stored.
In the data analysis stage, all the computations, data modeling, data cleansing, and linear aggregation models are ——————. When it requires data from a particular —————-, the data analysis component directly contacts the data store ————–. To ensure a highly responsive interaction with the system, the data analysis component also performs ————————— and applies proprietary algorithms.
performed
stream
layer
extensive caching
The data analysis component is ?
accessible through HTTP RESTful API.
The response to any request received by this component is?
in the format of JSON object.
The fourth stage is data visualization in which the output of the data analysis layer is rendered by ?
the front end and appropriate visualization components. This layer typically uses HTML5 for rendering and visualization.
Finally, the end user layer may comprise ?
a plant biologist or a farmer.
Based on the phenonet-OpenIoT integration, a farmer or scientist can perform an experiment through?
discovering relevant sensor data.
For instance,
an end-user searches for soil moisture sensors at different depths and composes an experiment/service for each of the discovered sensors.
The location of the sensor is mapped to the ?
node location of existing Phenonet application.
The metadata is added to the description of the experiment when?
composing the experiment using the request definition tool.
In addition, HTML5 visualizations of Phenonet are replaced with ?
OpenIoT request presentation tools that allow users to visualize the experiment’s outcomes.
In general, the use of the OpenIoT infrastructure for supporting the Phenonet project is based on?
several core modules and services of the OpenIoT platform.
For instance,
X-GSN is used as the sensor streaming middleware.
In this context, X-GSN functions are to be developed to interface with ?
the Phenonet data store to obtain real-time access to Phenonet sensor data.
Additionally, X-GSN also annotates incoming sensor data streams from?
the Data Store/Field.
The data from existing data store are pushed into LSM along with?
sensor annotations allowing discovery of sensor data.
The request definition and presentation tools are used to design and deploy an experiment with?
the help of the discovery, scheduler, and SDUM core services.
It is worth mentioning that the sensor configuration task requires?
the extension of the OpenIoT ontology toward describing Phenonet sensors, as well as the registration of the extended ontology with the cloud store (LSM).
Once data is pushed by X-GSN to the cloud database, the data becomes?
discoverable using the existing OpenIoT tools, namely the scheduler and request definition Interfaces.
The request definition interface is used to?
demonstrate the discovery of sensor data along with simple proof-of-concept services composition.
In general, building a smart grid dictates providing an energy supply for?
everyone in a rapidly growing population with a limited power production capacity.
To this end, a smart grid is designed to?
reduce losses,
increase efficiency,
optimize the energy demand distribution, and realize large-scale renewable energy, e.g., solar and wind deployments.
To enable different energy sources in a dynamic way, the grid topology has to be?
shifted from a centralized source to a distributed topology.
In this context, IoT technology can extend the connected benefits of the smart grid beyond?
the distribution, automation,
and monitoring.
Specifically, in-home and in-building smart devices will help ?
consumers in mentoring their own usage.
In addition, IoT sensors can be used to?
monitor occupancy and lighting conditions.
Accordingly, the electrical load can be?
automatically adjusted during off-peak energy hours.
To track real-time energy consumption and demand to the energy supply, it is highly necessary to?
deploy remote sensing equipment, referred to as smart meters, capable of measuring and communicating energy consumption data.
Based on this data, we can:
implement a self-healing grid,
increase the overall efficiency,
and increase the level of self-monitoring and decision making.
Accordingly, the first key step toward realizing an IoT-based smart grid is?
the mass deployment of smart meters.
Furthermore, a communication network is significantly required to?
connect all the different energy-related equipment in the grid.
Below, a smart electrical meter is demonstrated which does not only measure energy, but also?
provides a two-way communication system.
Initially, the smart meter comprises the energy transducer together with?
a processing unit to manipulate the collected data so that it becomes eligible for transmission.
Specifically, the processing unit needs to support advanced functions :
like dynamic pricing/demand response, remote connect and disconnect,
network security,
over-the-air downloads and post-installation upgrades so utility providers do not have to send out technicians to each meter.
As can be seen below, a typical smart grid comprises several communication systems, including:
Home Area Network (HAN),
a Neighborhood Area Network (NAN),
and a Wide Area Network (WAN), e.g., power line communication.
The HAN layer manages?
- the consumers’ on-demand power requirements and
-connects electrical appliances with smart meters.
It mainly comprises smart devices, home appliances—————————————————————————————————————— electrical vehicles, as well as renewable energy sources (such as —————————).
(including washing machines, televisions, air conditioners, refrigerators, and ovens),
solar panels
HAN is deployed within?
residential units,
in industrial plants,
and in commercial buildings.
The second layer, NAN (also known as ————————– (FAN)), consists of:
Field Area Network
smart meters belonging to multiple HANs.
NAN provides a communication facility between?
distribution substations and field electrical devices for power distribution systems.
It collects the service and metering information from multiple HANs and transmits it to the?
data collectors which connect NANs to a WAN.
WAN serves as a backbone for?
communication between network gateways or aggregation points.
It facilitates the communication among:
power transmission systems,
bulk generation systems,
renewable energy sources,
and control centers.
Modern smart meters must meet?
certain criteria to play such a critical role in the smart grid domain.
First, smart meters must report ?
energy consumption information from houses and buildings back to the utilities.
Depending of ——————————————————–, the communication facility will be selected.
the country and the nature of the grid
Depending of ——————————————————–, the communication facility will be selected.
the country and the nature of the grid
For instance,
the U.S. employs low-power RF communication using a Sub-1 GHz mesh network, while France and Spain employ wired power line communication (PLC) technologies.
Unfortunately, there exists no one connectivity solution that fits?
all deployments.
Therefore, realizing IoT-based smart grid requires a larger portfolio that can go from wired to?
g
The second criterion is that?
a smart meter must deliver useful power consumption information into the home through an in-home display or a gateway.
To sum up, electrical meters are becoming smart sensors that communicate both ways, inside and outside homes and buildings, connected to each other in a?
mesh network while reporting essential energy data to utilities.
In general, a smart grid contains four main subsystems:
power generation, power transmission, power distribution, and power utilization.
Thanks to the unique features of IoT, it can be applied to?
enhance all these subsystems.
For instance,
in the area of power generation,
IoT can be utilized for monitoring and controlling energy consumption
, units, equipment,
gas emissions and pollutants discharge, power use/production prediction,
energy storage,
and power connection,
as well as for managing distributed power plans,
pumped storage, wind power, biomass power, and photo-voltaic power plants.
In the area of power transmission, the IoT technology is employed for?
monitoring and controlling the transmission lines and substations, as well as for transmission tower protection.
In the area of power utilization, the IoT can be used for?
smart homes, automatic meter reading, electric vehicle charging and discharging, for collecting information about home appliances’ energy consumption, power load controlling, energy efficiency monitoring and management, power demand management, and multi-network consumption.
In this section, we discuss an IoT-based smart grid architecture that has a primary objective of improving the energy efficiency of buildings. It is worth mentioning that buildings consume a large portion of the total energy consumption worldwide. For instance,
buildings in the U.S. are responsible for about 71% of the total electrical energy consumption.
Accordingly, it is crucial to design green buildings which avoid?
unnecessary energy consumption.
In this context, the IoT technology enables users to monitor their consumption using their smartphones or PCs.
Accordingly, users can suit their own schedules at will, and respond to events instantaneously by?
adjusting the policies as and when necessary.
The figure below shows an energy-efficient location-based automated energy control IoT framework.
It uses smartphones and cloud computing technologies.
The architecture is comprised of four main components, namely:
multi-source energy saving policies,
monitoring and control via mobile devices
, location-based automatic control,
and a cloud computing platform for data storage and computation.
The targeted buildings are campuses, departments, labs, and rooms/offices, each with different energy requirements and policies that are crucial to consider?
while managing energy consumption.
Even in a single home, each family member has his/her own energy consumption preference which needs to be?
thoroughly considered. Hence, the smart grid framework, depicted below, has a tree-like structure control plane.
It consists of several layers of energy saving policies at different levels, e.g., building, department, lab, and room.
In fact, currently available smartphones are equipped with multiple networking interfaces, such as:
Wi-Fi, 3G/4G, and Bluetooth.
Moreover, the embedded GPS sensors are useful for obtaining precise location information.
Thanks to these unique features, smartphones represent an ideal candidate for remotely monitoring energy control systems.
After an initial authentication and authorization, consumers can dynamically modify their?
energy-saving policies through their smartphones.
To this end, consumers can simply interact with the policy servers of their home and their office buildings.
For instance,
a smart automatic control policy may rely on the location information of smartphones for switching energy consuming devices ON or OFF in homes and office buildings in accordance with the location and direction of user movements.
Such dynamic adjustment policies enable the coordination between?
buildings’ policies.
For instance,
when a user moves a predefined distance range from her home toward a predefined an office building, a message will be sent to the policy server of the office building.
Such messages trigger a policy control process, e.g.,?
start the heating.
The home appliances will simultaneously transit into an energy saving mode while the office building will start the processes based on?
the user’s preferences, such as cooling/heating to the user’s desired temperature.
As seen above, the framework employs the cloud computing platform for?
data storage,
modeling,
and analysis.
The cloud provides the basic data storage and retrieval services for?
each building’s energy consumption data.
The cloud provides the basic data storage and retrieval services for?
each building’s energy consumption data.
The cloud provides the basic data storage and retrieval services for?
each building’s energy consumption data.
The cloud platform performs mostly all computation-intensive modeling and analysis jobs. Furthermore, the cloud platform provides :
security,
reliability,
and configurability for the network communication between the cloud and the user.
The cloud has an application layer which contains:
a user-friendly web interface for managing the buildings’ energy systems so that
a remote user can easily configure and manage the system.
In general, the progressive aging of the population triggers significant attention on how to?
provide care to people in order to ensure a decent quality of life, without imposing drastic changes of habits.
In this context, the IoT technology has the potential to place personal smart-health systems hosting new inter-connections between?
the natural habitat of a person, his body, and the Internet.
Specifically, IoT can be applied in different domains, including:
patients care,
monitoring medical assets,
maintaining vital equipment,
and tracking equipment usage.
Specifically, IoT can be applied in different domains, including:
patients care,
monitoring medical assets,
maintaining vital equipment,
and tracking equipment usage.
Specifically, IoT can be applied in different domains, including:
patients care,
monitoring medical assets,
maintaining vital equipment,
and tracking equipment usage.
Specifically, IoT can be applied in different domains, including:
patients care,
monitoring medical assets,
maintaining vital equipment,
and tracking equipment usage.
Specifically, IoT can be applied in different domains, including:
patients care,
monitoring medical assets,
maintaining vital equipment,
and tracking equipment usage.
Specifically, IoT technology enables patients to?
remotely receive care away from their hospital at home, or even elsewhere around the world.
With wearable sensors and service solutions, doctors can reduce?
readmissions and enable proactive care.
Through monitoring medical assets, IoT allows the medical staff to spend less time searching by?
better tracking and managing supplies and medicine.
Along a similar line, IoT can help in providing critical medical devices when patients need them. This provision occurs through ?
fixing potential problems before they occur with predictive maintenance.
IoT can also enhance the overall well-being of patients by tracking how ?
equipment is used, from employing hospital bed sensors to monitoring room temperature and hand-washing stations.
An IoT-based healthcare system connects all the available resources as a network to perform healthcare activities such as:
diagnosing, monitoring, and remote surgeries over the Internet.
Diagnostic devices are significant for improving healthcare delivery. For instance,
the in-vitro diagnostics (IVD) solutions, such as those provided by the industry-leader Roche Diagnostics, greatly helps clinicians in detecting diseases, determining causes, and monitoring patient progress.
Such diagnostic solutions incorporate sophisticated underlying technologies, e.g., ?
clinical chemistry and immunoassays, urinalysis, point-of-care testing, patient self-testing, and laboratory automation.
To deliver these services cost-effectively, Roche Diagnostics explored the adoption of IoT technologies to address several requirements, including:
-remotely monitoring and managing IVD devices as fixed assets. In fact, Roche Diagnostics provides its IVD solutions to customers across China. Therefore, an effective way to monitor and manage them remotely is highly required to provide individualized and high-quality post-sales services that meet customers’ expectations.
-optimizing device availability with predictive maintenance. It is also necessary to predict potential downtime of any IVD solution deployed in a customer’s clinical setting by analyzing the operational data. Accordingly, Roche can dispatch supporting technicians to prevent breakdowns, avoid downtime, and maximize service life.
-recommending the best IVD solution for a customer’s needs. To this end, the actual usage data of the IVD solutions is to be thoroughly analyzed.
To achieve these requirements, Roche Diagnostics (2017) conducted a careful evaluation of different IoT cloud solutions. Selecting the most suitable cloud solution depends on three requirements:
architectural maturity;
speed of development;
and security.
In this use case, they selected a combination of?
the Microsoft Azure IoT solution accelerators and IoT Hub as the platform on which to build its IoT solution.
With the Azure IoT solution accelerators and IoT Hub, Roche Diagnostics provides an effective platform to?
connect and manage its IVD devices intelligently and remotely.
With the Azure IoT solution accelerators and IoT Hub, Roche Diagnostics provides an effective platform to?
connect and manage its IVD devices intelligently and remotely.
With the Azure IoT solution accelerators and IoT Hub, Roche Diagnostics provides?
an effective platform to connect and manage its IVD devices intelligently and remotely.
In turn, these capabilities enable the company to collect operational data, such as:
location, in near real time from those devices.
The company can now :
assess each IVD system’s health data,
troubleshoot any issues,
and, if problems arise,
trigger alarms to alert and dispatch supporting teams for service.
Based on the Microsoft Azure IoT platform, Roche Diagnostics is now able to provide enhanced value to customers in several ways, including:
improving monitoring of fixed assets, offering better management and provisioning of supplies, advising on optimizing asset utilization with additional solutions, providing data visualization and analytics for better decision making, and establishing a foundation for future predictive maintenance.
In this section, we explain how the Korean industry-leader 365mc adopted AI technology in the healthcare sector. In this realm, AI-based solutions can be extremely effective in discovering and healing disease, simulation education, surgery development, and more. 365mc is famous for?
providing dieting and liposuction treatments for patients to have a healthier life beyond focusing only on body shape.
Recently, 365mc YEAR combined AI technology with liposuction. Specifically, liposuction is a surgery that removes fat from the human body with a cannula—a tube that can be inserted into the body.
365mc’s M.A.I.L. System leverages IoT technology to bring data together from?
the sensor of the cannula that drains fat and the movement of surgeon and uses machine learning technology to pattern and analyze collected data from the surgery.
In fact, extracting fat is an extremely complex surgical procedure.
In particular, the cannula must precisely reach the fatty tissue between the skin and the muscles.
If not precisely done, the cannula may damage muscle tissue if it is injected into deeper tissue.
In addition, the fat may be removed unevenly or skin necrosis can occur if the cannula is not sufficiently inserted. Therefore, the skill of the surgeon is essential. In fact, the process of injecting a cannula and extracting fat occurs 12,000–20,000 times per surgery.
This fact means that moving a cannula in a uniform manner is critical.
Nevertheless, every patient may have different types of fatty tissue and different areas being operated on.
Owing to the complexity of the procedure, a skillful surgeon with vast experience is highly recommended.
In this context, 365mc had an idea to digitize the liposuction procedure by ?
leveraging motion capture technology. Such digitalization is necessary for raising the precision of the surgery, checking the progress after the operation, and providing the direction for surgeons to assure the high level of the operation.
To this end, 365mc realized that the digitalization idea into the————System powered by Microsoft ————–. Microsoft Azure provides Microsoft Azure IoT solution accelerators and Microsoft Azure Machine Learning which ——————————————-data from sensors in real time. In this use case, 365mc YEAR gathered the data of two billion ————movements over two years and derived valuable data from the analysis.
M.A.I.L.
Azure
collect, analyze, and store
cannula
Technically speaking, 365mc employs a special cannula having :
the motion recognition sensor.
This sensor keeps reading the activity of the surgeon’s hands in real time during the liposuction procedure.
The motion recognition sensor detects the trace of the cannula through correctly fusing readings from an accelerometer and a gyroscope.
Exactly 180,000 data entries can be generated per surgery, as one cannula movement makes nine types of data where the cannula moves a maximum of 20,000 times per operation.
To store and process this massive amount of data, Microsoft Azure IoT solution accelerators store is incorporated.
The M.A.I.L. System significantly changes the liposuction of 365mc:
-from depending only on a surgeon’s sensitivity to relying on data.
-Moreover, the M.A.I.L. System helps to create awareness of abnormal cannula movement immediately, allowing for the preparation of side effect countermeasures directly after the procedure.
-Overall, the M.A.I.L. System increased both the accuracy and safety of liposuction surgery.
In the future, 365mc looks forward to further advancing its systems with Microsoft Azure Machine Learning. An example of the futuristic plans of 365mc (n.d.) is ?
to enable the M.A.I.L. System to give notice to surgeon who showed a lot of unusual actions during surgery to check his/her body condition. Furthermore, 365mc plans to leverage data in various areas.
For example,
it built and used its own weight-loss record tools for wearable devices to digitize slimming-down programs for patients. Because the senses of human beings are unique and sensitive, they can become distorted. Luckily, the sensor, data, and AI smartly complement this imperfection and support the enhancement and maintenance of accuracy.
In this section, we discuss an IoT-supported healthcare system, referred to as the ————————- (SHS). Essentially, the SHS system collects, in real time, both —————– conditions and patients’ ————————parameters and delivers them to a control center. Subsequently, an advanced monitoring application analyzes the received data before sending alert messages in case of emergency.
smart hospital system
environmental
physiological
The figure below demonstrates the SHS architecture and is composed of three main components:
(1) the RFID-enhanced WSN, called HSN; (2) the IoT smart gateway;
and (3) the user interfaces for data visualization and management.
The first component comprises an integrated RFID-WSN 6LoWPAN network which is composed of four typologies:
6LowPAN border routers (6LBR), 6LowPAN routers (6LR),
6LowPAN router readers (6LRR),
and 6LowPAN host tag (HT).
According to the 6LoWPAN standard, the 6LBR is responsible for?
connecting the network to the Internet by translating 6LowPAN packets into IPv6 packets and vice versa, whereas the 6LR provides forwarding and routing capabilities.
In the SHS system, the ——- are defined as ——– nodes interfaced with ———–readers. The HT nodes represent a typical ——————, i.e., a node without routing and forwarding capabilities, interfaced with an ———————-.
6LRR nodes
6LR
RFID
6LowPAN Host
RFID tag
In fact, SHS assumes that several 6LR are deployed in a hospital to collect data from the environment, e.g.,?
temperature, pressure, and ambient light conditions.
In addition to the sensing capabilities, the main function of 6LRR nodes is to ?
track patients, nursing staff, and biomedical devices labeled with RFID tags. In particular, patients have to wear an HT node, which is capable of detecting important physiological parameters, e.g., heartbeat and movement/motion.
The collected data is periodically reported to the IoT smart gateway via?
the 6LRR nodes deployed in the environment. The IoT smart gateway bridges between the HSN nodes and the Internet through a LAN.
In the SHS architecture, the monitoring application, running on the gateway, analyzes the received data and stores it into the database————————————–. To make the collected data easily accessible by both —————————————-users, the REST web-based paradigm has been adopted.
Specifically, a web-based graphical interface allows network operators to manage environmental parameters of ———————–nodes.
(e.g., control DB)
local and remote
sensor and actuator
The same interface enables doctors with ?
-granted privileges to access both real-time and historical patient data.
-Such information can also be managed remotely by the medical staff through a customized mobile software application.
- Furthermore, doctors can be equipped with a smartphone connected to a portable RFID reader and running a customized application, known as Medical App.
Through the Medical App, doctors can interact directly with the HT node worn by?
the patient during the daily medical inspections in hospital.
Accordingly, doctors can check the physiological parameters of their patients by ?
reading the most recent information stored into the user memory of the RFID tag or historical information stored into the control DB.
In addition to reading data, the Medical App allows doctors to update the memory content with important information, e.g.,?
the last visit, changes of patient therapy, and health examinations.
Through exploiting the RFID-WSN integration, the SHS architecture can manage emergency situations in a timely manner. In case of critical events, e.g.,?
patient falls or heartbeat irregularities, the HT node exploits its long-range, high-power, reliable IEEE 802.15.4 radio transceiver to send a quick notification to the monitoring application.
This strategy improves the energy efficiency of HS nodes by allowing them to use the RFID radio interface for routine operations, e.g.,?
medical inspections, data logging, identification/tracking, while keeping the IEEE 802.15.4 radio OFF for most of the time. Accordingly, the battery lifetime of the HS nodes is highly maximized.
At the IoT smart gateway, the monitoring application sends notifications to inform the nursing staff about the patients’ location, i.e.,?
the last position where the RFID tag has been read, and their health status.
In fact, the collected data is sensitive and confidential, hence?
the platform must ensure an adequate level of security for data access and management.
Accordingly, users need to be authenticated before they can?
access the platform.
Obviously, the mobile app has be properly configured to guarantee the desired level of security. Furthermore, it is crucial to provide ?
a secure communication channel, since the interaction between the remote application and the SHS is performed through the public Internet.
To this end, the SHS platform employs a virtual private network channel for ?
connecting the mobile devices with the IoT smart gateway.
The IoT can be implemented successful in several application scenarios. This unit gives a short overview on interesting use cases in the industrial, agricultural, electrical and medical fields.
The application of IoT in the industrial field, denoted as Industrial IoT or IIoT, can help:
achieving higher throughput,
eliminate “wasteful” activities,
prevent failure,
increase product quality,
increase the flexibility of the value chain.
The application of IoT in the industrial field, denoted as Industrial IoT or IIoT, can help:
achieving higher throughput,
eliminate “wasteful” activities,
prevent failure,
increase product quality,
increase the flexibility of the value chain.
Smart agriculture refers to ?
the application of IoT in agriculture. Here the challenge is the large-scale collection and processing of sensor data to optimize crop management.
A smart grid is an intelligent power grid designed to ?
reduce losses,
increase efficiency,
optimize the energy distribution
and realize large-scale renewable energy.
A smart grid can :
dynamically enable different energy sources and adjust the load.
Smart meters, capable of?
measuring and communicating energy consumption data of users, are important components of a smart grid.
The IoT technology is showing its potential for innovative healthcare systems, for instance :
in patients care, assets monitoring, and equipment usage tracking.
With wearable sensors and service solutions, doctors can reduce readmissions and enable proactive care.
Remote monitoring also helps?
implementing better healthcare policies, generating “participatory” medical knowledge.
