AI is a type of computer science that is razor focused on developing intelligent systems capable of replicating human-like cognitive skills such as learning, reasoning, and problem-solving. It covers a broad spectrum of methodologies, incorporating elements such as computer vision, natural language processing, and machine learning. Conversely, the Internet of Things (IoT) pertains to an extensive network of physical objects integrated with sensors, software, and connectivity, facilitating the gathering and sharing of data across the internet. These interconnected devices range from everyday things like smart home appliances to complex industrial machinery and healthcare wearables.
AI and IoT have already demonstrated their transformative potential individually, reshaping industries and enhancing various aspects of our lives. However, the true power lies in their convergence. By integrating AI with IoT, organizations can create intelligent and connected systems that collect, analyze, and act upon real-time data. This combination unlocks a new realm of possibilities, empowering businesses to make data-driven decisions, automate processes, and deliver personalized experiences. From optimizing supply chains and predictive maintenance to revolutionizing healthcare and enabling smart cities, integrating AI and IoT paves the way for unprecedented advancements and efficiencies.
Let’s explore the seamless integration of AI and IoT and its profound implications across industries. We will explore the synergistic effects of combining AI’s cognitive abilities with IoT’s extensive data collection capabilities, showcasing the real-world applications, benefits, challenges, and best practices of creating intelligent and connected systems through AI and IoT integration.
Let’s dive deeper into understanding AI and IoT.
What is AI (Artificial Intelligence)?
Artificial Intelligence is a field of study that aims to create machines capable of exhibiting human-like intelligence. It encompasses various techniques, including machine learning, natural language processing (NLP), computer vision, and robotics. Machine learning, in particular, enables systems to learn from data and improve their performance over time without explicit programming.
Natural Language Processing (NLP) empowers computers to comprehend and analyze human language, while computer vision enables machines to recognize and interpret visual data extracted from images and videos. These AI subfields have found numerous applications across industries, including virtual assistants, recommendation systems, fraud detection, and autonomous vehicles.
What is IoT (Internet of Things)?
The term “Internet of Things” pertains to an extensive network of tangible objects embedded with sensors, software, and connectivity, facilitating their ability to gather and exchange data via the Internet. These “smart” objects range from consumer devices like home appliances and wearables to industrial equipment, agricultural sensors, and urban infrastructure. IoT devices continuously collect and transmit data from their surroundings to central servers or cloud platforms for further analysis and decision-making. The adoption of IoT has increased across industries due to its potential to optimize operations, enhance safety, improve energy efficiency, and enable data-driven insights.
What are the benefits and applications of AI and IoT Independently?
AI and IoT have individually revolutionized various sectors and use cases. With its advanced algorithms, AI has enabled personalized recommendations in e-commerce, improved customer service through chatbots, optimized supply chain operations, and detected fraudulent activities in financial transactions. IoT has enabled remote monitoring of industrial equipment for predictive maintenance, improved healthcare outcomes through remote patient monitoring, enhanced energy efficiency through Smart home automation, and transformed urban planning through Smart city initiatives. However, the real potential lies in integrating AI with IoT to create more intelligent and dynamic systems.
What does the synergy of AI and IoT result in?
A. How does AI enhance IoT?
AI enriches IoT by utilizing its sophisticated analytics and cognitive abilities to extract valuable insights from the immense data volumes produced by IoT devices. IoT devices collect vast amounts of data, often in real-time, making it challenging to analyze and interpret manually. Through the prowess of AI-driven analytics, data can be swiftly processed, uncovering patterns, anomalies, and trends that might elude human operators’ detection. For example, AI algorithms can analyze sensor data from industrial equipment to detect early signs of potential failures, enabling predictive maintenance and minimizing downtime. By incorporating AI into IoT systems, businesses can achieve higher automation, efficiency, and responsiveness levels.
B. How does IoT enhance AI?
IoT enhances AI by providing rich, real-world data for training and fine-tuning AI models. AI algorithms rely on large datasets to learn patterns and make accurate predictions. IoT devices act as data collectors, continuously capturing data from the physical world, such as environmental conditions, consumer behaviour, and product usage patterns. This real-world data is invaluable for AI models, allowing them to understand the context in which decisions are made and adapt to dynamic environments. With more IoT devices deployed and data collected, AI models become more accurate and responsive, leading to better decision-making and actionable insights.
C. What are the advantages of combining AI and IoT?
Integrating AI and IoT presents several advantages beyond what either technology can achieve individually. The combination enables real-time data analysis and decision-making, leading to more responsive systems and quicker insights. The continuous feedback loop between IoT devices and AI models ensures ongoing optimization and adaptation to changing environments. Additionally, the ability to automate processes based on AI analysis of IoT data streamlines operations reduces human intervention, and improves overall efficiency. Ultimately, integrating AI and IoT empowers businesses to transform data into actionable intelligence, leading to smarter decisions, better user experiences, and new opportunities for innovation.
What are the key components of AI and IoT integration?
A. Sensors and Data Collection:
At the heart of IoT are sensors, which serve as the eyes and ears of the interconnected system. These sensors are embedded in physical objects and devices, capturing temperature, humidity, motion, location, and more data. The insights gleaned from data collected by these sensors offer valuable information about the surrounding environment, empowering AI algorithms to analyze and make well-informed decisions grounded in real-world data.
B. Data Processing and Analysis:
IoT generates a staggering amount of data, often in real-time, which requires robust data processing and analysis capabilities. Edge computing plays a vital role here by processing data locally at the network’s edge, reducing latency, and ensuring real-time responsiveness. Cloud computing enhances edge computing by providing scalable and resilient data processing capabilities, empowering AI algorithms to analyze extensive datasets and extract actionable insights.
C. Decision-Making and Automation:
AI algorithms leverage the processed IoT data to make data-driven decisions, including forecasting maintenance needs, optimizing energy consumption, and identifying anomalies. These decisions, in turn, initiate automated actions, such as scheduling maintenance tasks, adjusting device parameters, or alerting relevant stakeholders. Integrating AI-driven decision-making and automation results in heightened system efficiency and proactivity, saving time and resources while enhancing overall performance.
D. Real-time Insights and Predictive Analytics:
AI algorithms can generate immediate insights and responses to dynamic conditions by analyzing real-time IoT data. For instance, AI-powered Smart home systems can adjust thermostats, lighting, and security settings in real-time based on occupancy patterns and environmental conditions. Additionally, predictive analytics based on historical IoT data can anticipate future trends, enabling businesses to take proactive measures and capitalize on emerging opportunities.
Let’s look at AI and IoT integration use cases.
A. Smart Homes and Home Automation:
AI and IoT integration in smart homes enables homeowners to create intelligent, energy-efficient living spaces. AI-powered virtual assistants, like Amazon Alexa or Google Assistant, can control IoT devices such as smart thermostats, lighting systems, and security cameras. This integration allows homeowners to automate tasks, adjust settings remotely, and receive real-time insights into energy consumption, leading to cost savings and enhanced convenience.
B. Industrial IoT and Predictive Maintenance:
In industrial settings, AI and IoT integration revolutionizes maintenance practices. Sensors embedded in machinery continuously monitor equipment health and performance, providing real-time data to AI algorithms. AI-driven predictive maintenance can detect anomalies and potential failures, enabling proactive maintenance to prevent costly downtime and improve operational efficiency.
C. Healthcare and Remote Patient Monitoring:
AI and IoT integration have the potential to transform healthcare by enabling remote patient monitoring and personalized care. IoT-enabled wearable devices can continuously monitor vital signs and transmit data to AI-powered healthcare systems.By employing AI algorithms, this data can be scrutinized to identify initial indicators of health concerns, offer tailored suggestions for treatment, and notify medical experts during urgent circumstances.
D. Smart Cities and Urban Planning:
AI and IoT integration is crucial in creating smart cities with improved infrastructure and services. IoT sensors deployed across urban areas collect data on traffic flow, air quality, waste management, and energy usage. AI algorithms analyze this data to optimize transportation routes, reduce congestion, manage waste more efficiently, and enhance urban planning.
E. Transportation and Autonomous Vehicles:
The fusion of AI and IoT is driving the advancement of autonomous cars. IoT sensors provide real-time data on road conditions, weather, and vehicle performance. AI algorithms process this data to make split-second decisions, enabling autonomous vehicles to navigate safely and efficiently on roads.
What are the challenges of AI and IoT integration?
A. Data Security and Privacy Concerns:
The extensive volume of data produced by IoT devices gives rise to worries regarding security and privacy. Integrating AI means handling even more sensitive information, increasing the potential for data breaches and cyber-attacks. Ensuring robust data security measures and adhering to privacy regulations are crucial in mitigating these risks.
B. Interoperability and Standardization:
The diverse range of IoT devices from various manufacturers may need more standardized communication protocols, hindering seamless integration with AI systems. We addressed interoperability challenges to enable smooth data exchange between IoT devices and AI platforms.
C. Scalability and Complexity:
As the number of IoT devices and data volume grows, the scalability and complexity of AI systems increase. We ensured that AI algorithms can handle the ever-expanding data streams, and computations become paramount for successful integration.
D. Ethical and Social Implications:
The use of AI and IoT raises ethical considerations, such as data ownership, algorithmic bias, and potential job displacement due to automation. Striking a balance between technological advancement and ethical responsibilities is essential to ensure that AI and IoT integration benefits society responsibly.
What are the best practices for successful integration?
A. Data Governance and Management:
Implementing robust data governance and management practices is crucial for AI and IoT integration. Define clear data ownership, access controls, and sharing policies to ensure data security and compliance. Additionally, establish data quality assurance processes to maintain accurate and reliable data for AI analysis.
B. Robust Security Measures:
Address the security challenges of AI and IoT integration by adopting strong encryption, secure communication protocols, and authentication mechanisms. Regularly update and patch IoT devices to protect against vulnerabilities and potential cyber-attacks. Employ multi-layered security measures to safeguard data and infrastructure.
C. Collaboration between AI and IoT Teams:
Foster collaboration between AI and IoT teams to ensure a cohesive approach to integration. Encourage regular communication, knowledge sharing, and joint problem-solving. The combined expertise of both groups can lead to innovative solutions and effective AI and IoT implementation.
D. Continuous Monitoring and Improvement:
Monitor the performance of AI algorithms and IoT devices continuously. Gather input from users and stakeholders to pinpoint areas for enhancement and possible concerns. Regularly update AI models and software to adapt to changing data patterns and maintain peak performance.
What does the future of AI and IoT integration look like?
The future of AI and IoT integration is a promising landscape, marked by transformative advancements that will reshape industries and daily life. As AI algorithms gain the ability to analyze vast amounts of real-time data from interconnected IoT devices, decision-making processes will become more innovative and more proactive. This convergence will lead to the rise of autonomous systems, revolutionizing transportation, manufacturing, and urban planning.
The seamless integration of AI and IoT will pave the way for personalized experiences, from Smart homes catering to individual preferences to healthcare wearables offering personalized medical insights. As edge AI and federated learning become prevalent, we addressed privacy and data security concerns, allowing for decentralized and efficient data processing.
Ethical considerations and regulations will be crucial in ensuring responsible AI and IoT deployment, while sustainability practices will find new avenues through efficient energy management and waste reduction. The future holds boundless possibilities, with AI and IoT poised to usher in a connected world, transforming how we live, work, and interact with technology.
The future holds boundless possibilities, with AI and IoT poised to usher in a connected world, transforming how we live, work, and interact with technology.
While the vision for interconnected networks of “things” has existed for several decades; its execution has been limited due to an inability to create end-to-end solutions. Particularly the absence of a compelling and financially-viable business application for wide-scale adoption.
Decades of research into pervasive and ubiquitous computing techniques have led to a seamless connection between the digital and physical worlds. Facilitating an increase in the consumer and industrial adoption of Internet Protocol (IP)-powered devices. Several industries are now adopting creative and transformative methods for exploiting the ‘Code Halo’ or ‘data exhaust’ that exists between people, processes, products, and operations.
Currently, there are endless opportunities to create smart products, smart processes, and smart places, nudging business transformation across products and offerings. Smart connected products offer an accurate insight into how customers use a product, how well the product is performing, and a fresh perspective into overall customer satisfaction levels. Moreover, companies that previously only interacted with their customers at the initial purchase can now establish an ongoing relationship that progresses positively over time.
Future Promise – Business Transformation through IoT
Let’s begin with considering the immediate future – in the next few years, the term ‘IoT’ will cease to exist in our vernacular. The discussions will instead shift to the purpose of IoT and the business transformation that is realized. We will see the emergence of completely new business models, products-as-a-service, smart cities, intelligent buildings, remote patient monitoring capabilities, and industrial transformational models. Order-of-magnitude improvements will be at the forefront as business intelligence boosts efficiency, waste reduction, predictive maintenance, and other forms of value.
The capturing of ambient data from the physical world to develop better products, processes, and customer services will be a core aspect of every business. The conversation will shift from how things are to be ‘connected’ and focus more on the insights gained from the instrumentation of large parts of the value chain. IoT technologies will become a commodity.
The real value will be unlocked through the analytics performed on the massive streams of contextual data transmitted by the ‘digital heartbeat’ of the value chain. IoT will form the crux of how products operate and the way physical business processes progress. In the future we expect the instrumentation-to-insights continuum to become the standard method of conducting business.
Layers of an IoT Architecture
Incorporating connectivity, computation, and interactivity directly into everyday things is dependent on organizations and requires an in-depth understanding of industry business problems, new instrumentation technologies and techniques, and the physical nature of the environment being instrumented.
Generally, IoT solutions are characterized by three-tier architecture:
IoT Architecture
Physical instrumentation via sensors and/or devices.
An edge gateway, which includes communication protocol translation support, edge monitoring, and analysis of the devices and data.
Public/private/hybrid cloud-based data storage and complex big data analytics implemented within enterprise back-end systems.
Successful business transformation initiatives leverage these IoT tiers against a specific industry challenge to gain a market advantage. Lastly, these IoT integrations should be configured to the actual physical environments in which the instrumentation technology will be deployed and aligned with the business focus areas for each organization. This usually requires organizations to leverage third-party expertise or various other complementary sets of ecosystem partnerships.
Scalability Challenges in IoT
With the explosion in market share, aspects such as network security, identity management, data volume, and privacy are sure to pose challenges and IoT stakeholders must address these challenges to realize the full potential of IoT at scale.
Network Security: The explosion in the number of IoT devices has created an urgent need to protect and secure networks against malicious attacks. To mitigate risk, the best practice is to define new protocols and integrate encryption algorithms to enable high throughput.
Privacy: IoT providers must ensure the anonymity and individuality of IoT users. This problem gets compounded as more IoT devices are connected within an ever-expanding network.
Governance: Lack of distinguished governance in IoT systems for building trust management between the users and providers leads to a breach of confidence between the two entities. This situation happens to be the topmost concern in IoT scalability.
Access Control: Incorporating effective access control is a challenge due to the low bandwidth between IoT devices and the internet, low power usage, and distributed architecture. This necessitates the refurbishment of conventional access control systems for admins and end-users whenever new IoT scalability challenges occur.
Big Data Generation: IoT systems carry out programmed judgments leveraging categorized data gathered from numerous sensors. This data volume increases exponentially and disproportionately to the number of devices. The challenge of scaling lies in large silos of Big Data generated as determining the relevance of this data will need unprecedented computing power.
Similar to most technology initiatives, the business cases are realized only when these technologies are implemented at scale. The connection of only a few devices isn’t enough to harness the full potential power of IoT for developing more meaningful products, processes, and places to elevate business performance.
What Companies Get Wrong About IoT
Avoid a fragmented approach to IoT
Typically, companies, especially large multinational corporations that have global footprints do not have a clear owner of IoT within the organization. This leads to a fragmented and decentralized decision-making process when it comes to IoT.
For example, consider a company that has many factories across the world. Each factory has a bespoke application and a bespoke vendor for providing a single discrete use case. Each factory works well when we consider its individual silos, however, it is very difficult to gain an aggregated view across the entirety of the company as a whole. This leads to problems with scaling as the company is structurally limited, resulting in the company having to scale back to begin implementing and reengineering the process from the ground level.
When it comes to the IoT agenda, multinational companies need to be mindful of the short term and long term, at a global and a local level, to effectively capture IoT value. It is imperative to unite the business processes with technology as well as instill a change in mentality towards IoT value to derive real change within these companies. This includes having a completely different approach towards KPIs, incentives, and the performance management of people on a very practical level.
Overcoming the Challenges of IoT Scale
To rapidly progress from prototyping to real-world deployment, it is essential to focus on the challenges of scaling IoT:
1. Zero in on the underlying business problem or opportunity.
Change the mindset surrounding IoT with regards to technology experimentation leading to business transformation, starting with the company’s most valuable assets. A well-orchestrated engagement between the COO and CIO, a CFO-ready business plan, product, delivery, and customer service is a prerequisite for effectively scaling IoT.
2. Learning how IoT amplifies value.
Whenever an object is integrated into an IoT system, it acquires a unique persistent identity along with the ability to share information about its state. As a result, the value of an intelligent object is amplified throughout its lifecycle – from creation, manufacturing, delivery, and subsequent use, till its demise. This also includes its network of suppliers, producers, partners, and customers, whose interactions and access are handled by the IoT. During IoT exploration, whenever a product’s lifecycle and network are taken into account, it paints a clearer picture of the potential for structural transformation of processes, networks, and even the product itself.
3. Consider the Physical Nature of the Environment.
IoT provides connectivity to everyday objects that are rooted in a physical place. This leads to two critical dimensions of IoT scaling:
An understanding of the interplay between objects, between objects and people, and between objects and the environment (which further necessitates a deep understanding of the setting and inner workings of the physical place).
An understanding of how the physical environments themselves might affect the connectivity and successful interaction of objects. As IoT is reliant on wireless radio waves to transmit data from objects, any radio interference in a physical environment can impact transmission and must be considered during system design.
IoT scale aims to ensure that individual systems communicate with each other within the physical world and become invisible, blending seamlessly into the workplace. This requires a deep understanding of the inner workings of the physical place and the ability to translate technology within said environment. For instance, a “digital oilfield” IoT concept might foster a relationship between oil and gas consultants that understand industry pressures, drilling rig personnel that know the physical nature of day-to-day operations, and IoT technology experts capable of calibrating and connecting the devices within the environment.
4. Embrace the concept “it takes a village” to unite all IoT ingredients.
IoT is a “system of systems” composed of several different ingredients and expertise, dependent on end-to-end systems integration. These elements can fuel a transformation within a business model and develop coordinated initiatives designed for scale. Enrolling partners with the necessary domain expertise, and with a reputed history of integrating IoT technologies, will be key for establishing a long-term roadmap for IoT strategy and implementation.
An Integrated Approach Is Necessary For Driving End-To-End Transformation Across Business, Organization, And Technology
Realizing Full IoT Value
Adaptive organizations will quickly transcend IoT workshops and pilots to establish a long-term roadmap that is fueled by their business’ vision for the future and not technology. IoT can be incredibly disruptive and valuable across an industry, meaning that early adopters helping companies understand how to bring basic connectivity within their organization, will often fall short of unlocking the underlying business value that can be realized at scale. To make a meaningful impact on the business model, the product, and/or operational processes, businesses must implement IoT in a coordinated effort – across functions – at scale. This necessitates vision and leadership, outside expertise, and an ecosystem of partners for delivering a successful IoT journey.
NeoSOFT’s Use Cases
All over the world, businesses are looking to scale their IoT processes from different perspectives; some start by exploring new sensing technologies and how they can be applied to their processes, others search for ways to enhance and advance their existing data sources through new data mining techniques. As their products acquire new characteristics through IoT instrumentation, businesses have to re-imagine their products and develop ways to deliver new and value-driven services for their customers.
Listed below are some of the highlights of our work in providing innovative and scalable IoT solutions:
Developing futuristic, robust, and reliable smart home security solutions
Engineered a home security solution that makes it easier and convenient for customers to monitor their household security remotely. Our engineers developed an intuitive hybrid mobile interface capable of integrating multiple smart guard devices within a single application. The solution leveraged remote monitoring, home security, and system arming/disarming managed via AWS IoT services.
Taking retail automation and shopping convenience to the next level with AI and IoT-powered solutions
A fully automatic futuristic store that leverages in-store sensor fusion and AI technology. Our goal was to leverage and connect all store smart devices, including sensors, cameras, real-time product recognition, and live inventory tracking. Data analytics on smart devices led to the creation of personalized and customer-driven marketing efforts.
Exploring new possibilities in human health analytics
The client is an innovator in the field of medical imaging for the detection and spread of cancer and other abnormalities. Our task was to leverage advanced technologies to accurately detect its presence and spread within the lymph nodes using IoT, AI, and 3D visualization.
Decades ago, the idea of embedding sensors and chips into physical objects would sound insane and impossible. Today, Internet of Things has drastically transformed most aspects of our everyday life such as driving, cooking, purchasing, etc. The number of IoT units in different industries is projected to amount to 30.9 billion units worldwide by 2025.
The manufacturing industry is also seeing a rapid rise in the deploying of IoT. Many plants have already incorporated connected control systems for their operation and supervision. The primary benefits of IoT solutions include:
Helping to detect and avoid issues that may cause delays.
Increasing production quality of an industrial unit and reap benefits from raw stuff, and manufactured components using cognitive operations.
Enabling the managers for better allocating resources, improving worker skillfulness and making the work environment safe.
Currently, IoT is popularly harnessed to help deal with facility and asset management, security and operations, logistics, customer servicing, etc., making it a highly promising trend in the manufacturing sector.
Here are some of the best IoT use cases in manufacturing —
1. Predictive Repairing
By connecting IoT-driven gadgets with different sensor points (temperatures, vibration, voltages, currents, etc.) to other devices, IFTTT, cloud/API or legacy systems manufacturers can obtain essential maintenance data. This kind of information allows them to estimate the current condition of machinery, determine warning signs, transmit alerts, and activate corresponding repair processes.
This transforms maintenance into a fast-paced and automated practice, which foresees a failure well in advance. Moreover, this kind of predictive repairing saves cost exponentially compared to the traditional preventive measures since the actions are taken exactly when they are necessary.
By getting valid data on time, managers can detect issues and plan maintenance operations. This helps in prolonging equipment lifetime, contributing to plant safety, and lowering the risks of accidents that affect the environment negatively.
2. Remote Production Control
Reallocating your company’s computational resources to a custom cloud or connecting the device to one of the popular BaaS (backend as a service) or PaaS (platform as a service) cloud computing models, you can collect and analyze large-scale data sets necessary for supervising various field devices like switches, valves, and other indication elements.
Thanks to IoT, this data can be transmitted to the industrial automation system which then ensures an overall control of machinery amidst the production process.
Telecommunications, oil and gas industries, as well as power generation, have all been already reaping the benefits from IoT devices implanted into distant control systems.
The most prominent feature of remote production control in the industrial automation system is the centralized supervision over the machinery in the process of production. Information obtained through distant control provides a much clearer and faster insight into the actual production field.
This assists in analyzing the enterprise data, making IoT technology a core instrument in ensuring safe automated production, workforce monitoring, and personnel location tracking.
3. Asset Tracking
IoT technology combined with the development of native web and mobile apps for iOS or Android makes it possible to obtain real-time asset information and make reasonable decisions.
In tracking, the primary task is to discover and oversee the crucial assets such as the components of the supply chain — raw materials, containers, and finished goods. IoT integrated asset tracking apps can drastically help to optimize logistics, maintain stocks of work in progress, and disclose thefts and violations.
IoT-based asset tracking also helps the producers to calculate the usage of movable equipment elements and initiate measures to shorten the idle period and enhance utilization.
4. Logistics Management
Enterprises that depend greatly on transportation can also benefit from IoT-led interconnection between various devices and systems.
IoT can be employed to reveal supply chain inefficiencies by eliminating blind spots from logistics processes. Managing the automotive fleet via IoT-driven devices (autonomous fleet solutions) helps manufacturers eliminate or put down the risks concerning the costs related to vehicles, staff and transportation — contributing to the greater efficiency of the company.
Logistics managers can make good use of IoT when it comes to repairs and fuel expenditures by optimally monitoring fuel costs, smart deliveries, diagnostics, and drivers.
Additionally, a real-time overlook of driver and vehicle performance aids in raising technicians’ safety, bringing down inventory damage and reducing insurance payments.
5. Digital Twins
Digital Twins is an IoT approach that lets businesses create and enjoy robust digital copies of the physical objects manufactured by a company.
When empowered with IoT, a POC (proof of concept), an MVP (minimum viable product) or a look-and-feel prototype can be turned into an accurate digital copy. This can then be used to easily experiment on and foresee their functionality as well as initial and final operational capabilities.
This kind of IoT application can create a simulation of machines’ lifespans — which can be helpful in checking updates and predicting potential issues and bottlenecks.
With IoT instruments, producers can get a replica of equipment or goods that can be monitored in a virtual environment before releasing them in the market.
Finally, it helps to improve product quality, create efficient supply and delivery chains, open new opportunities for businesses, and propel customer service to new heights.
Implementing IoT solutions into your manufacturing plant or your commercial business process need not be a monumental hassle. With experts backed by credible hands-on field-experience, our team is highly equipped to provide custom, robust, end-to-end IoT solutions designed to empower and give your business an edge over your competition.
IoT (Internet of Things) technology is penetrating deeply into everyday human life. Cars, kitchen appliances, and even heart rate monitors connected via the Internet into one network where they exchange data. For example, having received data from the alarm clock in your smartphone, an IoT coffee maker will know when you get up for work and brew coffee at your desired time, down to the minute.
However, one of the most promising areas for the implementation of IoT devices is in healthcare. According to a study, six out of ten global healthcare organizations are already using IoT devices.
There are several positive trends observed due to this:
Medical staff are becoming more mobile.
The process of collecting, transferring, and analyzing patient data, as well as making a diagnosis, is accelerated.
The effectiveness of medical care is increasing.
IoT Devices for Patients
Patient monitoring sensors are the area where IoT comes the most in handy. Being placed in operating theatres, intensive care units, and post-surgery rooms, these devices will monitor the vitals of patients, and in case of dangerous situations, immediately notify doctors.
Such devices will help not only doctors and patients but also their relatives. For example, if a patient is going to have a difficult operation, an online location sensor can be attached to the patient’s body, to which their family can immediately know when the surgery is finished and receive its results.
A special sensor-based inpatient monitoring platform is being used which reads the patient’s vital signs round the clock and allows the medical staff to instantly respond to deterioration. In the future, there are plans to equip patients with such devices in cardiology and intensive care departments.
Another good example is a wearable device, which can predict a forthcoming epileptic seizure.
In many regions, there is still no easily accessible medical care. The transition to telemedicine has become an effective solution for this situation. A patient far away from any clinic or hospital is able to consult with a doctor in real-time and receive the necessary assistance before going to a medical facility.
Medical Equipment With IoT Technology
Monitoring sensors for hospital equipment can significantly improve the quality of medical services. Due to limited budgets, medical facilities can’t afford frequent replacements of necessary equipment. As a result, outdated equipment is in constant need of repairs.
IoT sensors can assess the state of the equipment and inform engineers about defects. This will allow for a quicker response time to breakdowns.
IoT devices can also help in monitoring the condition of the hospital premises. For instance, the sensor will take temperature readings in laboratories, freezers, and wards. If the temperature deviates from the norm, it will be possible to return it to the desired level remotely via Wi-Fi.
When transporting medicines that require a certain temperature, refrigerators with similar sensors can be useful. They will maintain the necessary temperature independently.
The Role of IoT in Medical Facility Management
IoT can also be deployed in solving the administrative and management challenges of the hospital. For instance, with the help of IoT devices, it is possible to keep track of the number of pharmaceuticals, the condition of the equipment, as well as identify the need to purchase replacements.
The Netherlands is already adopting a similar strategy. One hospital has a network that allows its staff to view available equipment and get quick access to patient data. This helps to avoid confusion and reduces the waiting time for medical procedures.
Navigation in huge hospital complexes is a challenge for patients and their families. A special application can help them find the required doctor’s office or ward by creating a route inside the building.
In Conclusion:
The future of IoT is extremely promising. The recent events have only highlighted the potential of harnessing IoT, Artificial Intelligence and Machine Learning for their improved efficiency and the factor of safety they offer in the field of healthcare — even in times of unimaginable crisis as that of COVID-19.
At NeoSOFT, we empower businesses and healthcare by helping them leverage the right IoT technology that suits their requirement and needs. Our experts work end-to-end on solutions that go beyond the realm of problem-solving and strive to provide meaningful value-addition. Contact us to discuss your ideas and we will realise them with an uncompromising promise of efficiency and safety.
Ambient Intelligence is set to rise in its scope and potential as machine learning continues advancing and the number of IoT devices and sensors continue increasing.
Ambient Intelligence (AmI) is a new paradigm in information technology that’s rapidly transforming the healthcare industry. What started out as merely a concept – by tech company Philips, and European Commission’s Information Society and Technology Advisory Group (ISTAG) – in the 1990s is, today, an amalgamation of two, primary, disruptive technologies – Artificial Intelligence (AI) and Internet of Things (IoT). It is because of AmI that the world has witnessed impressive development in AI assistants like Siri, robotics, sensors and more. With thoughtful use, this technology is on the crux of disrupting healthcare too.
What is Ambient Intelligence?
Ambient Intelligence refers to the combination of IoT sensors, sensor networks, and Human-Computer Interaction (HCI) technologies powered by Pervasive-Ubiquitous Computing, big data and artificial intelligence frameworks. Or in simpler terms, they are physical spaces capable of being sensitive and responsive to the presence of humans. This technology paves the way to a futuristic world where sensors embedded in daily use devices will create an intelligent environment which adapts to its user’s needs and wishes seamlessly.
AmI can be leveraged in a wide range of technologies such as biometrics, affective computing, RFID, Bluetooth low energy, microchip implants, sensors like the thermometer, motion detectors, photo-detectors, proximity sensors, and nano-biometrics. These sensors will gather data, and interpret and analyze it to adjust to or predict user expectations.
Ambient intelligence-powered environments have the following characteristics:
Awareness of individuals’ presence
Recognition of their identities
Awareness of the context (e.g. weather, traffic, news)
Recognition of activities
Adaptation to the changing needs of every individual
How Will It Help Healthcare?
Early applications of AmI could enable more efficient clinical workflows and improved patient safety in ICUs and operating rooms. It can –
Help by recording patient health stats (with patient permission) and update the patient Electronic Medical Record (EMR) to provide a better and more accurate narrative.
Aid health care workers (physicians and nurses) in delivering quality care by analyzing patient information like prior treatments, allergic responses of the patient and more.
Help the elderly by remotely monitoring their health and enables them to have an independent living, in countries with a higher population of senior citizens. (Through Ambient Assisted Living (AAL) technology.)
Enrich overall patient experience, physician satisfaction, and quality of care.
Smart Hospital Rooms
Ambient intelligence can pioneer smart hospital rooms equipped with AI systems that can do a range of things to improve outcomes. The School of Engineering at Stanford University is reportedly exploring how a combination of electronic sensors and artificial intelligence could be installed in hospital rooms and elder care homes to help medical professionals monitor and treat patients more effectively.
It suggests using two types of infrared technologies, i.e. the low-cost active infrared and passive detectors which can be incorporated into the patient environment. The first type of infrared is already being used outside hospital rooms, for instance, to discern whether a person washed their hands before entering and, if not, issue an alert. The second infrared technology, i.e. the passive detectors will help night vision goggles to create thermal images from the infrared rays generated by body heat.
In the hospital setting, a thermal sensor above an ICU bed would enable the governing AI to detect twitching or writhing beneath the sheets, and alert clinical team members to impending health crises without constantly going from room to room. During the research, passive detectors helped the team of researchers avoid relying on high-definition video sensors since capturing video imagery could unnecessarily infringe the privacy of clinicians and patients. Meanwhile, the active infrared helped them in tracking hospital-acquired nosocomial infections. Leveraging such ambient intelligence applications can also help in computer-assisted monitoring of patient mobilization in ICUs, and automating surgical tool counts to prevent objects from being accidentally left in a patient.
Takeaway
Ambient Intelligence is still emerging. Currently, it has already empowered users’ capabilities via the creation of a sensor-based environment which is sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions.
In healthcare, it will help in numerous ways like continuous monitoring, smart hospitals, assisted therapy, etc. Not only that, but Ambient Intelligence is also on the threshold of disrupting businesses and industries like e-commerce, retail and more. With the proliferation of IoT devices, Ambient Intelligence will surge, however, company vendors should be careful about factors like data usage, privacy and overall security.
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