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In the era of the internet, where there are more than 6 billion connected devices, and petabyte-scale data is flowing in seconds, IoT or Internet of Things analytics is the next big thing. Before we discuss the analytics part, let’s look at the definition of IoT from Wikipedia ‘The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Now the data collected by these devices can also be used to make decisions without manual intervention or rule-based applications. Let’s discuss how they are taking place in the industry.
Start Your Free Data Science CourseWhy do we Use IoT Analytics and its Real-World Applications?
It is a field of data science where data from sensors and connected electromechanical systems are analyzed and converted to valuable business insights. Industry grade IoT applications are called as IIot(Industrial Internet of Things).
Let’s see the Industrial applications of IoT analytics.1. Manufacturing Industry
It has been changing the industry landscape for manufacturing sectors. Smart sensory data are used to prevent faults or breakdowns, requirement analysis, and resource optimization. IoT solutions help organizations in smart asset management, performance monitoring, which reduces asset downtime and increases hardware longevity. It also enables manufacturers with a lower time to marketability and large-scale customizations. For example, IoT helped bike manufacturer Harley Davidson to reduce the time to produce a complete bike from days to hours.2. Healthcare
The popularity of smart wearables is increasing day by day. This enables researchers with more and more data to incorporate IoT solutions. Data from wearables are used to prevent heart attacks. IoT-based solutions with nanotechnology are even used to monitor cancerous cells inside the body.3. Home Automation
Switching on the air conditioner before arriving home or switching off lights from a different location is no longer science fiction, It’s already commercially available. IoT analytics is used to make decisions and optimize power consumption automatically. Google Home, Amazon echo, etc., are examples of some of the IoT-based home automation devices where analytics and machine learning are used heavily.4. Automobile and Transportation
In the internet era, automobiles are also considered gadgets where upgrades can be made on-demand. They are being used for collision prevention, smart parking, and even for self-driving cars. The whole research area of self-driving cars is based on deep learning models based on data obtained from IoT devices like LIDERs and image sensors.5. Insurance
As an Industry, Insurance seats on a gold mine of data, insurers slowly started adhering to analytics in their industry solutions. As per the Gartner report, it will change the industry landscape by 2023. IoT solutions can be used for automated claims processing, automated reserve setting, damage assessment, etc. In the case of automobile claims, image data based on deep learning solutions are incorporated.6. Weather Forecast
One of the most important use cases of it is in weather forecasting. Weather stations and satellites collect atmospheric data every second. This data can be used to forecast extreme weather conditions like floods, drought much earlier. IoT solutions are also being used for automatically controlling the water levels in dams.7. Energy Sector 8. Telecommunication
The hardware deployment and maintenance cost for the telecommunication sector is always a pain for the telecom industry. It is helping telecom players to analyze bandwidth consumption, tower management, fault analysis, automated hardware maintenance with very little or no manual interference.Trends Typical IoT Analytics Flow
Typical analytics use the following steps:
1. Data Collection: A collection of data from IoT sources like audio, image, light sensors. Handling streaming data is a great challenge for IoT applications.
2. Preprocessing of Data: The preprocessing of collected data is a tricky part of machine learning use cases. Suppose the feature engineering for heartbeat sensor data will be much different from the data collected in weather stations. But that’s where the art part of data science/Analytics lies.
4. Train and Test: After preprocessing, and EDA, various machine learning and deep learning models are trained as per use case and business requirements. Business and technical KPIs are decided upon a case basis. Based model is chosen through cross-validation, and offline and online testing is performed.
5. Deployment and Prediction: This is the part where systems act upon the insights gathered from the analytics solution. Based on the model performance, it is retrained or recalibrated.
The flow of a typical IoT analytics use case.Conclusion
This article discussed the high-level view of IoT analytics, its industrial use cases, global trends in IoT analytics, and sample workflow of an IoT analytics use case. Despite the increasing demand and applications of it, there is another face of it. The concern of privacy can not be denied at all. Strong and balanced data governance is needed to build and maintain a sustainable end-to-end IoT ecosystem.Recommended Articles
This is a guide to IoT Analytics. Here we discuss the introduction and the use of IoT Analytics and its Real-World applications. You can also go through our other suggested articles to learn more –
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In this article, we aim to examine the concept of IoT architecture, explain the difference between IoT ecosystem and IoT architecture, demonstrate its ten different components, and finally provide a real-life example for contextualization.What is IoT architecture?
IoT architecture comprises several IoT building blocks connected to ensure that sensor-generated data is collected, transferred, stored, and processed in order for the actuators to perform their designated tasks.What is the difference between IoT ecosystem and IoT architecture?
IoT ecosystem is the encompassing term attributed to the five general components of devices, communication protocols, the cloud, monitoring, and the end-user in the IoT system.
IoT architecture is the breakdown of the inner workings of these building blocks to make the ecosystem function.What are the different elements of IoT architecture?
For the sake of brevity, we will only explore the ten most important parts of an IoT architecture.1- Devices
IoT devices are equipped with sensors that gather the data, which will be transferred over a network. The sensors do not necessarily need to be physically attached to the equipment. In some instances, they are remotely positioned to gather data about the closest environment to the IoT device. Some examples of IoT devices include:
Cameras and CCTVs2- Actuators
Actuators are devices that produce motions with the aim of carrying out preprogrammed tasks, for example:
Smart lights turning on or off
Smart locks opening or closing
Thermostat increasing or decreasing the temperature3- Gateways 4- Cloud gateways 5- Data lake
A data lake is a data storage space that stores all sorts of structured and non-structured data such as images, videos, and audio, generated by IoT devices, which will then be filtered and cleaned to be sent to a data warehouse for further use.6- Data warehouse
For meaningful insight, data should be extracted from the data lake to the data warehouse, either manually, or by using data warehouse automation tools. A data warehouse contains cleaned, filtered, and mostly structured information, which is all destined for further use.7- Data analytics
Data analytics is the practice of finding trends and patterns within a data warehouse in order to gain actionable insights and make data-driven decisions about business processes. After having been laid out and visualized, data and IoT analytics tools help identify inefficiencies and work out ways to improve the IoT ecosystem.8- Control applications
Previously, we mentioned how actuators make “actions” happen. Control applications are a medium which, through them, it’s possible to send out the relevant commands and alerts which will make actuators function. An example of a control application could be soil sensors signaling a dryness in the lawns, and consequently, the actuators turning on the sprinkles to start irrigation.9- User applications
They are software components (e.g. smartphone apps) of an IoT system that allow users to control the functioning of the IoT network. User applications allow the user to send commands, turn the device on or off, or access other features.10- Machine learning
Machine learning, if available, gives the opportunity to create more precise and efficient models for control applications. ML models pick up on patterns in order to predict future outcomes, processes, and behavior by making use of historical data that’s accumulated in the data warehouse. Once the applicability and efficiency of the new models are tested and approved by data analysts, new models are adopted.What is a real-life example IoT architecture?
The sensors take relevant data, such as daylight or people’s movement. The lamps on the other end, are equipped with actuators to switch the light on and off. The data lake stores these raw data coming from the sensors, while a data warehouse houses the inhabitants’ behavior on various days of the week, energy costs, and more. All these data, through field and cloud gateways, are transferred to computing databases (on-premise or cloud).
The users have access to the user application through an app. The app allows them to see which lights are on and off, or it gives them the ability to pass on commands to the control applications. If there is a gap in algorithms, such as when the system mistakenly switches off the lights and the user has to switch it on manually, data analytics can help address these problems at its core.
When daylights get lower than the established threshold, it’s the control applications commanding the actuators to turn the lights on. At other times, if the lights are on power-saving mode and would only be turned on if a user walks past the lawn, it’s the cloud that receives the data of a passerby walking and after identification, alerts the actuators to turn the lights on. This makes sure that false alarms are detected and the power is conserved.
But the control application does not only function with already-established commands. By leveraging machine learning, algorithms would learn more about usage patterns and customize the functionality accordingly. For example, if the inhabitants leave home at 7 am and come back at 5 pm, after some time, the lights would turn off and on in between this interval autonomously. These smart adjustments would, furthermore, reduce the need for human intervention and make for seamless continuity.For more on the internet of things
To learn more about the technical side of internet of things, read:
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He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.
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While the Honor View 10 is the latest flagship from the Huawei sub-brand, it still comes with an affordable price tag. Offering premium features like Dual cameras, 18:9 aspect ratio, and an AI-backed processor, the phone looks promising in its price segment. We got our hands on the Honor View 10 and here is our review of the company’s latest flagship.Honor View 10 Specifications
Key Specifications Honor View 10
Full HD+ (2160 x 1080p)
Android 8.0 Oreo with EMUI 8.0
Kirin 970 processor
Yes, up to 256GB
Dual 16GB RGB + 20GB Monochrome lens
Sim Card Type
157mm x 74.98mm x 6.97mm
Rs. 29,999Physical Overview
The Honor View 10 comes with a 5.99-inch FullView display, with a resolution of 2160 x 1080 pixels. Honor has managed to tone down the bezels despite using a front-mounted fingerprint sensor, which is good. The camera and sensor array are placed above the display, along with the earpiece.
With the volume rockers and lock button placed on the left side, you get the SIM tray and microSD card slot on the right side of the Honor View 10.
At the bottom, the phone sports a 3.5mm earphone jack along with a USB Type-C port which enables fast charging. The top of the phone sports an Infrared BlasterDisplay
In real life, we found the viewing angles to be good, thanks to the IPS panel. Also, it is a crisp display even in bright sunlight and can be dimmed enough for comfortable low light usage too. We found the panel to be snappy and responsive.Camera
The Honor View 10 sports dual rear cameras at the back. This setup comprises a 16MP RGB lens and a 20MP monochrome lens. The monochrome lens enhances the image detailing and helps in low light photography.Camera UI
When it comes to cameras, the user interface is what makes the experience smooth. Honor has optimized the camera app’s UI for the tall 18:9 display of the View 10. You can swipe left to access ‘Modes’ and right to access ‘Settings’.
What we found slightly difficult was to use the commands on the top of the screen. Since it is tall display, operating the camera on the Honor View 10 can be a little difficult with one hand for some people.Camera Samples Daylight Samples
While the wide aperture mode had some edging issues, the overall performance is good. The camera is quick to focus and captures good detail in daylight.Artificial light
In artificial light too, the Honor View 10 performs really well. There is no noticeable grain or noise. Depth is also captured fairly well by the cameras in artificial lighting.Low Light samples
Coming to low light photography, the monochrome lens on the device comes into play here. It captures images without any grains and uses the flash to focus and capture images without interfering with the details.Hardware
In terms of processor and storage, Honor has left no stone unturned with these specifications. You also get expandable storage up to 256GB using a microSD card. The gaming experience on the device is also smooth, thanks to the dedicated gaming mode.Software and performance
In terms of software, the Honor View 10 is running on the latest Android 8.0 Oreo update with EMUI 8.0 skin on top. While you get the latest Android version, EMUI makes the experience more optimized. It is a fluid user interface and is adaptive to your usage.
The View 10 has a dedicated gaming mode for uninterrupted performance. To prove its mettle, we tested it on certain benchmarking apps and here are the results.Battery and Connectivity
With all the key connectivity options (including the 3.5mm jack) intact, the Honor View 10 does a great job in terms of connectivity.Verdict
Competing against the likes of the OnePlus 5T, the Honor View 10 is available exclusively on chúng tôi starting today at a price of Rs. 29,999. ICICI credit card users can also avail a flat discount of Rs. 1500, and Airtel users can get up to 90GB data free.
The Internet of Things (IoT) is a network of interconnected devices & gadgets that can collect & share data by themself. IoT data analytics refers to the procedure of gathering, examining, and deciphering data produced by these devices to gain knowledge and make wise decisions. data analytics uses bunches of hardware, software, and data science techniques to collect accurate information from massive data created by IoT devices. An overview of IoT data analytics, its elements, and its applications are given in this article.Components of IoT Data Analytics
IoT data analytics involves four main components −
Data Collection − IoT devices are embedded with various sensors that collect data on different parameters such as temperature, humidity, pressure, and motion. This data is transmitted to a central server or cloud platform for further processing.
Data Storage − The data generated by IoT devices is massive and needs to be stored efficiently.
Data Processing − IoT data analytics involves processing data to extract valuable insights. To make sure the data is correct, consistent, and prepared for analysis, data processing procedures including data cleansing, data transformation & data normalization are utilized.
Data analysis − To find patterns and trends in the data, statistical & machine learning algorithms are employed.
Data Visualization − IoT data analytics involves the use of data visualization tools to present insights and findings in a user-friendly and understandable format. Visualization tools like dashboards, charts & graphs help to understand the data quickly and then make decisions in a very logical and practical way. So, they can give an informed decision based on the insights derived from IoT data analysis.Applications of IoT Data Analytics
IoT data analytics has several applications in various industries. Some of these applications are −
Predictive Maintenance − IoT data analytics is used to predict when equipment is likely to fail. By analyzing the data generated by sensors embedded in machines, organizations can identify patterns that indicate potential equipment failure. It enables organizations to schedule maintenance before a failure occurs, reducing downtime and increasing efficiency.
Energy Management − IoT data analytics is used to monitor and optimize energy consumption in buildings. By analyzing data on energy usage, temperature, and occupancy, organizations can identify areas where energy usage can be reduced. It helps organizations save money on energy costs and reduce their carbon footprint.
Supply Chain Optimization − IoT data analytics is used to optimize supply chain operations. By analyzing data on inventory levels, transportation routes & delivery times, organizations can identify areas where supply chain processes can be improved. It helps organizations reduce costs and improve customer satisfaction.
Smart Cities − IoT data analytics is used to make cities more efficient and sustainable. You can easily analyze traffic patterns, air quality, and energy usage. With this cities can identify the areas they need improvements.
Healthcare − IoT data analytics is used to monitor patients remotely, collect vital signs data & provide personalized healthcare. By analyzing patient data, healthcare providers can identify patterns that indicate potential health issues, enabling them to intervene early and provide more effective treatment. IoT data analytics can also help healthcare providers improve operational efficiency by optimizing resource allocation and reducing wait times.Challenges of IoT Data Analytics
IoT data analytics also presents several challenges. Some of these challenges are −
Data Security − IoT devices generate sensitive data that can be vulnerable to cyber-attacks. Every organization must make sure that IoT data is stored securely. Also, only authorized people can access it.
Data Privacy − IoT devices collect personal data such as location, health, and behaviour. Organizations should check that all these data must be collected and used in compliance with privacy regulations.
Data Quality − IoT data can be noisy and inconsistent. Organizations need to ensure that IoT data is accurate, consistent, and reliable for analysis.
Scalability − IoT data is generated at a massive scale. Organizations need to ensure that their IoT data analytics infrastructure can scale to handle large volumes of data.
Interoperability − IoT devices come from different manufacturers and have different protocols & standards. All these make it difficult to integrate & analyze data from different sources. Interoperability challenges can lead to data silos, reduced efficiency, and increased costs. Organizations need to ensure that their IoT data analytics infrastructure can integrate data from different sources and platforms seamlessly.Conclusion
IoT data analytics is an emerging field that has the potential to transform various industries. By addressing these challenges, organizations can unlock the full potential of IoT data analytics & realize the benefits it offers. Overall, IoT data analytics is a rapidly evolving field that offers significant opportunities for organizations. Organizations may gather, store, process, and analyze enormous amounts of data created by IoT devices to gain insightful knowledge by combining hardware, software, and data science tools.
SAN JOSE – Trust was the watchword in Bill Gates’ keynote speech, which opened the RSA Security Conference here yesterday. And he laid out a wide-reaching, four-pronged plan to improve trust and security in what he calls an ever-increasingly digitized world.
The security industry needs to help companies create trusted relationships with partners and customers, the Microsoft chairman and chief software architect told a packed audience of security professionals. And companies need to be able to trust the security of the code they’re using.
And smart cards, which Gates has promoted for years, are part of his latest plan to help users tread the fine line between trusted authentication and privacy concerns.
Gates’ plans for the future of security was long on vision and short on details. He talked about four initiatives: creating a trusted ecosystem, building more secure code, striving for simplicity, and building ‘fundamentally secure’ platforms.
He also announced a few new features in the upcoming Windows Vista client operating system, which include smart card support, changes to Internet Explorer and a new identity technology that he called InfoCard.
”All together this will create a trusted ecosystem,” he said. ”Secure code, devices and users… It’s a big challenge to make sure security is not the thing that holds us back.”
The Ultimate Question About Identity
Gates wasn’t the only one championing trusted relationships — whether they be between users, partners or applications.
Art Coviello, chief executive officer and president of RSA Security Inc., and Scott McNealy, chairman and chief executive officer of Sun Microsystems, followed Gates with their own morning keynotes at the security conference that is drawing in 14,000 attendees this year, according to an RSA spokesperson.
”Who are you?” asks Coviello. ”In the world of business, even the most basic transactions start with that question… We need to help people answer that question.”
And both Coviello and Gates say part of being able to do that will include the wide-spread — however long-awaited — adoption of smart cards.
”In terms of authentication, passwords are increasingly the weak link,” says Gates. ”We need to move to multi-factor authentication. A large part of that will be smart cards.”
Gates, however, is far from the first industry luminary to talk about the coming demise of the password and the rise of the smart card. So far, the death knell has not rung for the password, and the smart card, at least in the United States, has failed to catch fire.
Gates and Coviello both say that is about to change.
Smart cards won’t only be used for logon authentication. They’ll also be used to make online transactions easier and safer. Microsoft is banking on it. The upcoming InfoCard technology will be used, Gates notes, to authenticate users to various websites they do business with. The cards will offer up varying levels of information about the user, giving the least amount of information needed.
This is designed to aid companies and individuals walk the fine line between users’ privacy concerns and the need to authenticate their identities.
”I’m forsaking some elements of my privacy,” says Coviello said. ”There’s that friction between authentication and privacy… We are reaching the time when digital identification must decrease this friction.”
Coviello explained that every transaction does not call for the same level of authentication. A simple transaction might only call for a name and membership number, where as an online application for a bank loan or a money transfer would call for absolute identification.
”For too long organizations have blended into a one-size-fits-all identity scheme,” Coviello said, ”rather than find a well-fit approach to each transaction. We can take a layered risk-based approach.
”All the parties involved need to have confidence,” he adds. ”They’re choosing convenience over security [today]. How long do you think it can go on this way?”
Security Breeds Confidence
That confidence is hard to come by these days, said Sun’s McNealy, who showed the audience part of an email he received from a company stating that they had his name and Social Security number on a laptop that had been stolen. ”It’s a big issue and it’s a personal issue for every one of us,” he said.
McNealy also noted that several million people are being added to the Internet every week, and 390 gigs of content are being created every second. People are buying 200 million cell phones every quarter.
”Pretty soon, you’ll face your cell phone out and you’ll videotape your whole day,” he said. ”It’s going to get scarier if we don’t come up with technology and rules to secure that data.”
Part of the problem in securing that data, according to McNealy, is the “hodge-podge” of technologies linked together to build data centers that more resemble Frankenstein monsters than secure systems.
”You’ve got 87 suppliers, half of whom have been bought by Oracle recently, and you wonder why you have security problems,” he told the audience. ”I always argue that computer security is more screwed up than any other industry out there, except health care — and they kill everyone eventually. So the bar is pretty low.”
Hacking has always been a problem. Whether it’s cracking the code to a vase or the password to an email account, criminals have always exploited security weaknesses for personal profits.
Unfortunately, protecting privacy hasn’t been our strong suit in the digital age. Tech companies push the next line of products and organizations are hesitating to invest in cybersecurity programs for these devices that owners may not even comprehend is a difficult task.Waning cybersecurity resources
While most software programs provide at least some type of encryption, typically, it’s not the strongest. That is to say, that these software programs are not impenetrable. To effectively and safely utilize technology connected to an important or sensitive network requires education.
Also read: Best 10 Email Marketing Tools in 2023The majority of businesses are not prepared for liability
Most businesses are not prepared for a breach. However, this doesn’t stop them from updating technology or layering new technologies on top of legacy systems. As such, organizations need a solution that is easier to implement and maintain.
Spoofing, phishing, and social engineering are all trademarks of any legitimate hacker. However, each of these strategies is easily countered through cybersecurity education and training.Poor governance meets new complexity
Most companies are host to dozens of employees with little to no knowledge of cybersecurity and the different ways that hackers infiltrate databases and networks. This creates an environment with essentially dozens of moving access points with further access still to more sensitive networks internally.
Once a hacker is in the intranet of a company then the overall system must either go into lockdown or risk losing valuable data. Luckily, limiting this risk through education is quite simple as many cybersecurity companies offer education courses and much of the critical information is available for free online.
An educated workforce will undoubtedly defend against hacking attempts saving businesses an incredible amount in liability. However, avoiding critical or aggressive hacking attempts requires an overall security protocol that is typically nonexistent in today’s corporate world.
Also read: Best Video Editing Tips for Beginners in 2023Blockchain and security protocols
Security by design is usually lacking in IT governance. Managing the complexity of the Internet of Things is quite a difficult, if not impossible task. These unclear privacy protocols and insufficient cybersecurity tools make IoT an incredible liability for businesses of virtually all sizes.
To circumvent the collapse of IoT systems, especially in a corporate setting, emerging technologies like Blockchain must be integrated into current software products, security and otherwise.
The IoT has created opportunities for enterprises and entrepreneurs around the world by providing internet connectivity through as many channels as possible. Ironically, it is the same function that has provided a wealth of possibilities and targets for hackers.
This prevailing issue of cybersecurity isn’t new by any stretch. In fact, hacking has been an issue as long as the internet itself has existed with much of the hacking done through social manipulation. However, in recent times, hacking has upgraded quite a deal and is now more complex than ever before with hackers capable of exploiting any vulnerable node on a network.
Even with this reality, many companies still do not deploy sensible cybersecurity protocols that adequately deal with the issue. Much of the success that hackers enjoy is due to a fundamental lack of cybersecurity knowledge that plagues the personnel of the targeted business.
To sum up Blockchain could positively impact cybersecurity because:
It’s the technology behind cryptocurrency that enhances recordkeeping
A secure, immutable peer-to-peer network utilizing cryptography
Dynamically offloading connections to a serverless lightweight blockchain
Could help trap vulnerable parts of databases
After-market Blockchain cybersecurity systems could help companies (automating IoT security to some degree)
The IoT has been incredible for the world at large as the access to the internet has exponentially expanded and therefore so has the spread of information.
This culminates in a world in which any enterprising individual can start a business online and find phenomenal success no matter where they are in the world. However, even with all of the incredible benefits, the IoT has also worsened a problem that we collectively never really solved.
This can be accomplished in any number of ways but defending against these hacking attempts can be made simple by implementing a strict cybersecurity protocol.
However, protecting the database of your company may become much easier through the use of blockchain technologies that could be used to create transparent databases that still retain privacy and security. Blockchain databases are constantly checked for validity to guarantee that the information goes untouched.
Additionally, these datasets can be utilized in any number of ways to create other technologies or databases. Traditionally, databases are static record blocks that can be dynamically encrypted to deter hackers.
However, implementing positive cybersecurity protocols requires constant updating and revisions. The blockchain method may solve this database issue by creating an incredibly secure alternative that will keep the business safe and its clients.Romy Catauta
Romy Catauta works in the marketing field and is passionate about writing on how to hire a mobile app designer, web design, business, interior design, and psychology.
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