Big Data

Big Data is Changing the Outlook of the Renewable Energy Sector

How Big Data is Changing the Outlook of the Renewable Energy Sector?

The renewable energy sector is facing a significant transformation, and all credit goes to the power of big data. With its capability to gather, store, and analyze an immense amount of data in real-time, big data provides unprecedented insights into how we generate and use energy.

This has allowed companies in the renewable energy sector to create innovative solutions supporting us to adopt and create a more sustainable future.

So, let’s check how big data transforms the renewable energy sector and how the sustainable future will look!

Big data is a term used to describe the immense amount of data that organizations accumulate and analyze to achieve better insights into their operations. It can be sourced from various sections like:

  • Customer feedback
  • Transactional records
  • Sensor readings
  • Social media posts
  • Search queries, etc

All these together form a data set that can be utilized to make the most suitable decisions on the basis of analysis of correlations, ongoing patterns, and trends.

We can simply say that big data is a way to convert raw data into actionable insights, and this is what makes it so powerful.

Let’s know how big data functions

As we have already discussed, big data is utilized to collect and analyze vast amounts of data in real-time. This enables companies to understand consumer activities and behavior while optimizing their processes as well as operations.

Also, the analysis of big data can assist in identifying patterns that are unintentionally ignored. This is how companies are able to discover new opportunities and develop strategies accordingly. Not just this, big data also empowers organizations to get a better insight into their operations. 

For instance, energy companies can keep track of energy usage and identify areas where improvement is needed and efficiency can be enhanced.

Here, we can place the example of Tesla powerwall. It collects data from its solar panels to observe the production and consumption of electricity in real-time. Tesla’s power wall can be utilized to optimize energy usage by offering customers with customized options.

Three ways through which big data is transforming the renewable energy sector

So, at least, we have some knowledge of big data. Now, let’s find out how it is changing the renewable energy sector.

1. Improved Efficiency:

Big data analysis can support companies by identifying areas where efficiency can be enhanced in energy systems. For example, it helps in reducing wastage and optimizing output. This will ultimately improve the entire profitability produced by renewable energy businesses. This supports both seller and buyer as they can save energy costs and use the same to invest in other green-based projects or initiatives.

The skyrocketing cost of traditional energy sources has unveiled the importance of renewable energy. It has made it more attractive, and the involvement of big data can aid in making it more efficient. Big data will not only make renewable energy more feasible but will also make it a more attractive alternative for buyers.

2. Presaging Demand and Supply:

Big data can also be utilized to foretell the demand and supply of renewable energy.

By analyzing historical data, businesses can understand the market pattern and behavior and can easily calculate the present demand for distinct types of renewable energy resources. Therefore, they can change, shift, or adjust their production as per the need. In this way, companies can target a specific customer base, leading to more conversions and ultimately adding more profits. On the other hand, customers will also get whatever they want, so it turns out to be a win-win situation for everyone involved.

Other than predicting demand and supply, big data are also used to forecast weather conditions, which will allow businesses to plan their production of renewable energy resources.

For instance, the Tesla power wall can forewarn the weather conditions and shift energy production consequently.

3. Automation of a few processes:

In the end, we can say that the most significant advantage of having big data in the renewable energy sector is automation. By automating specific processes, organizations can save time as well as resources while making their operations more efficient.

For instance, solar panel systems can be linked to the internet and designed to adjust their output depending on weather conditions on a real-time basis. In this way, consumers can cut down their electricity bills by generating more energy when the sun is shining bright in the sky.

Besides this, companies can also utilize big data to automate the maintenance of their assets involved in renewable energy systems. By tracking and analyzing real-time data, they can interpret any issues and take action before they turn out to be a significant problem in the process.

Conclusion

With rising global temperatures and increasing greenhouse gases in the environment, it has become necessary to bend toward renewable resources for energy generation. The shortage of non-renewable resources and by-products it offers, like pollution, greenhouse gases, etc, is another reason to shift toward renewable energy resources.

In this initiation, the addition of big data is causing an immense impact on the renewable energy sector. It is making renewable energy more efficient by predicting demand and supply and automating a few processes to reduce time and cost. With the advancement of technology, in the coming years, big data will become an integral part of the renewable energy sector and churn the best result while promising a green and sustainable future.

IoT Digital Transformation is on the Way to Change the Business Outlook

IoT Digital Transformation is on the Way to Change the Business Outlook

The Internet of Things, also known as IoT, is the interconnectivity of physical devices, vehicles, people, and objects with sensors, software, and network interconnectivity, allowing them to collect and exchange data.

Today, it’s not hard to access or collect data; it’s readily available. However, many processes, machines, and other technologies still need to be fully connected and become something the industry deems smart. This digital transformation is all set to begin.

As per Grand View Research, the global IoT devices management market size was estimated at 1.88 billion in 2022 and is assumed to increase at a compound annual growth rate of around 34.9 percent from 2023 to 2030. The growing importance of enterprises on controlling linked devices and enhancing operational efficiencies across industry sections would lead to an increase in the demand for IoT device management.

The continuous growth of IoT gives a clear signal that it will stay for a long time and will highly help and impact shaping the future. Though some processes, machines, and other devices are yet needed to be connected, it’s just a matter of time before they will need to be integrated into this technology-driven world. This indicates that the future is strongly linked to IoT, and its increasing demand and day-by-day expansion prove this fact.

A Faster and More Un(predictable) World

The continuous advancement of technology is forcing businesses to adopt the Internet of Things. It promises to fulfill the desire for efficiency and has become a necessity. In today’s rapidly changing and advancing world, it has become mandatory to maintain efficiency, and if one fails, it will lead to a huge failure. ChatGPT‘s technology is best to quote as an example to support this. Surprisingly, for the first time, Google has stepped into difficulty as they ignored that technology must continuously update to keep a sync with the dynamic environment.

The Internet of Things has become a game-changing technology that offers more predictability in an unpredictable world. IoT allows the device to collect and analyze real-time data from connected devices, which can be utilized to predict and prevent potential problems before they happen.

IoT Driving Transformation on its Way…..

Digital transformation means integrating digital technologies into all sections and processes of a business, causing fundamental changes to how the business operates and delivering results to customers. The current status of IoT, with its swiftly evolving technology and the increasing adoption of interconnected devices, is all set to bring pace to digital transformation across various industries.

By authorizing businesses to gather and analyze immense amounts of data in real time, IoT provides:

  • The optimization of business processes
  • The generation of new revenue ways
  • The development of innovative business models

Integrating IoT devices into business operations allows businesses to gain insights into customer behavior, enhance operational efficiency and improve overall customer experience. Hence, the Internet of Things continuous advancement and expansion is expected to drive digital transformation across industries forcefully.

IoT is Adding More Meaning to Technology Advancements

The features offered by IoT have eased the prediction process and made it less intimidating. The very influential Internet of Things has positively covered the digital and physical world gap, offering a futuristic environment that can sync with the changing technologies instead of being left behind. It can gather and analyze flooding data from interconnected devices to provide meaningful insights into various aspects of lives and businesses.

Various challenges have appeared with the growth of the Internet of Things, yet it has persisted and achieved its current state. Starting from compatibility challenges to data security and scalability. These are some initial installation issues while implementing IoT technology in businesses.

Businesses that embraced IoT as a newcomer played a significant role in resolving issues via testing and establishing the technology. As a result, businesses can now leverage the faster and more dependable implementation of IoT solutions, enabling smoother integration into their operations.

Effortless Interoperability: The Way to Leverage Seamless Technology

The complexity of IoT can be overwhelming for many, creating confusion and uncertainty. When many devices are added to an IoT network, managing and scaling the infrastructure tends to be a big challenge. The increasing number of devices and the immense amount of data generated by them can crash the existing systems, creating a challenge to manage and analyze data properly.

Business owners usually need clarification about upgrading their equipment and the different technology stacks involved in IoT. Now, it’s high time to shift the focus from a complex technology stack to a simple solution.

Old technology and processes should not limit your ability to make informed business decisions. The solution is not to remove them but to connect and boost them by linking them with advanced technology.

We hope to see even more innovative applications in various sectors as IoT evolves. The growth prospect is high, and businesses adopting this technology will be in a better place and reap its benefits.

As per Mckinsey, The Internet of Things has now become part of more than 200 applications in enterprise environments and is not just limited to large corporations alone. Early adopters have done trials and testing and are scaling IoT solutions throughout their businesses. The features and versatility of IoT technologies have resulted in several remarkable applications in various sectors like smart cities, connected cars, smart buildings, smart homes, e-health, and many others.

The latest IoT technology advancement has enabled all sectors to access non-existent features. For example, Business-to-Business (B2B) companies are now using Industry 4.0 technologies to create direct connections with their products in the field.

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The Internet of Things has come a long way and has become essential to our lives and businesses. The continuous growth and development of IoT and the increasing number of connected devices, combined with the need for efficiency and relevance, make the technology imperative to be adopted. By adding IoT, businesses can churn the maximum benefits and make the existing processes more efficient and cost-effective.

How IoT Data Analytics Impact your business

What is the Impact of IoT Data Analytics on your Business?

Today, if we observe the trend and business processes, we can express that IoT solutions are changing the way of doing business globally. However, saying that all solutions provide equal benefits would be wrong. We can say that an IoT solution that shares data without analytics is like a symphony playing Mozart without a conductor. This means music is there but with no structure and loses its purpose, beauty, and meaning. We all know that there will be immense flooding of data by IoT, but the absence of a process to properly analyze the data would just cause complexity and noise without proper output.

The Impact of Data Analytics on businesses

Data is compelling and empowers by giving insights into all aspects of the business. It can assist organizations in refining processes, locating missing physical assets for cost saving, or even helping in defining new use cases for already available products. 

In the absence of data, a company can be just reactive or can assume future challenges and results. 

With the data offered by an IoT solution, a company can anticipate the emerging problem before it becomes complicated and resolve it as soon as possible. However, there are IoT data solutions that only offer the data and no other context to make it meaningful. In such cases, IoT analytics comes in as a savior. 

The capability to interpret the data before it comes in front of the user is compelling. For instance, data analytics can help alert a factory manager about the floor problem in real-time instead of waiting and then reading through reports on issues that have already happened. This can reduce time consumption and the possibility of errors. 

Analysis software is available in many forms, from one-size-fits-all products to low-code/no-code solutions to solutions that demand an experienced engineering team to execute and maintain.

Each type of solution has benefits and expenses, and your enterprise must determine the best-fitting solution to get the maximum benefit.

Well-known IoT Data Analytics Solutions

We all are aware that technologies like AWS IoT Analytics, on the one hand, are sophisticated and powerful but, on the other hand, very complicated to execute and demand a highly skilled engineering team having domain expertise. The advantages of the analytics solutions are- it offers customization. Everything needed in your business and unnecessary things to be left out. You can consider AWS IoT products like building blocks: you can get maximum from them, but they demand a lot of planning along with maintenance and oversight.

All businesses cannot afford or consider hiring an expert engineering team to execute these solutions. These businesses are inclined toward adopting a one-size-fits-all solution like Azure provides IoT Central

Azure even provides a solution analogous to AWS, but they are more successful in an out-of-the-box strategy. The straightforward analytics provided by this solution or any other one-sized solution can fulfill the requirements of many businesses. They enable businesses to connect promptly and design their dashboards and alerts within a few days or hours. If your business just needs simple alerting or has a limited number of devices to connect, then opting for this solution would be a great idea and cost-saving as well.

Customizable Solutions

The main challenge with the IoT data analytics solutions mentioned above is that they don’t provide customization options, are costly to scale, and might compel your team to do analytics using a third-party tool (which is no doubt another pricey option). Suppose you own a business having specific analytic requirements and many devices to be connected. In that case, a low-code/no-code solution, like the one proposed by Leverege (running on Google Cloud), could be a terrific middle-ground solution. This type of solution is customizable per the business’s requirement and, in parallel, does not need any technical expertise if it offers an end-to-end alternative and has analytics and an excellent alerting system, even without needing a dedicated and proficient engineering team. 

Irrespective of whatever solution you choose for a business to implement, ensure that a third-party tool to be integrated gives you maximum flexibility and value from the data. Tools like Power BI, Tableau, and Looker can be the best option to support your company in familiarly visualizing your data. If your company has already made a preferred analytics tool list, then it will enable your users to harness their expertise of that tool with new data sources.

Valuable Insights

Till now, hope that you have understood the importance and contribution of Analytics tools. It is essential to obtain the optimum value from IoT solutions irrespective of the products the business chooses. Neglecting these core capabilities may take your business to the loss side as it may miss valuable insights and maximize value. We can simply infer that IoT solutions, no doubt, enhance business operations but remain incomplete.

Data analytics gives direction and beauty to the solutions as it analyzes the data and offers favorable data to businesses to boost operations and amplify outcomes. Today, most companies are embracing the Internet of Things but are unaware of the importance of data analytics and ignore it. They face losses and then switch back to their old processes and operations. Therefore using IoT and offered IoT solutions must be opted for after attaining full knowledge.

Today, IoT is making its space in almost every sector, from smart homes to smart buildings, from smart towns to smart cities, and from smart farming to smart logistics; one can see the influence of IoT in every sector.

Similarly, data analytics is also contributing from its end to add more value to every solution offered by the Internet of Things. For instance, in the baking process, the availability of raw ingredients is insufficient, and it does not come together without a recipe. The recipe brings ingredients together in a beautiful way and offers the best. So, if you are still untouched by the magic of data analytics, then you might be losing a lot of benefits and leverages offered by it.

What is the Impact of IoT on Global Logistics Development

We all know that today, the logistics market is dynamic and has become competitive. In the last few decades, logistics has been redesigned not just because of rising competition and circumstances in the world but also because the Internet of Things (IoT) has dived deeper into the logistics niche.

As per KPMG reports, market challenges are compelling participants to find new development points for the business and recreate existing supply chains, like rail transit in the Asia-Europe direction. A high empty mileage decreases the efficiency of cargo transportation and causes congestion on the decided routes. Let’s look at modern IoT logistics solutions; and how they impact international logistics and transport.

What is IoT in logistics?

We can simply understand this technology through examples such as IoT, a modern smart refrigerator door that orders the delivery of your favorite pizza and drinks, or a smart kettle that brews your coffee in one click from a smartphone. There are smart sensors in agricultural fields and drones with high-pixel cameras that allows farmers to monitor the condition of the soil. The world will become an entire Internet of Things complex in a few more years. 

However, when we mention the word Internet of Things, the first relation of this smart and emerging technology links with smart devices and tools that are physically available. Yet, IoT goes far beyond this and especially in global logistics.

IoT Logistics Examples

With the reduced cost of technology, the size of IoT devices also decreases. It is now quite apparent that devices and instruments are getting smaller with the growing market. Smaller sensors gather a more significant amount of data through creative and non-destructive placement.

Let’s assess what modern developments have been designed for us besides the sensors.

Warehouse & Inventory Management using IoT

IoT sensors track inventory and furnish data that can be utilized in trend analysis to presage inventory needs. Goods are automatically repositioned with stacker cranes’ assistance, production time and labor costs are cut down, and the human factor is balanced because the robot does not need leisure hours. This will bypass under-stock and over-stock situations.

Tracking Goods From Purchase To Delivery

Traditional monitoring depends on scanning an order between points of delivery. Special tags like RFID or Radio Frequency Identification simplify the search operation by connecting to the cloud and sending location data more frequently than scanning. This might get you back to the QR codes or Data Matrix times. Yes, they can also be used by analogy, but unlike FID, optical codes have to be scanned individually for each item, which takes time.

RFID tags reduce unnecessary expenditure. On average, the precision of inventory levels is approximately 65 percent. Employing RFID raises it to 95 percent. BigData monitoring under RFID will identify the most persuasive couriers and truckers, choose the most efficient delivery routes, and more. If delivery staff show unexpected results, they are sent for further revisions.

Drone Delivery

Drones are remotely controlled and unmanned aerial vehicles and droids that can improve the speed and efficiency of various logistics infrastructures. It is no more a trend or novelty as today’s developments are improving the accuracy and speed of their movement. As per the CompTIA poll, drones are employed by companies of different sectors and sizes. They enable the automation of business processes and allow smart inventory tracking, fast product transportation, and prompt delivery from stores.

Future Insights of IoT in Logistics

The proliferation of the Internet of Things in the international logistics market generated $34,504.8 million in 2019. Prescient Strategic intelligence shows a steady CAGR of 13.2 percent by the end of 2030. Nowadays, crucial assignments of logistics companies are the following:

  • Assure just-in-time delivery.
  • Offer transparency in the supply chain.
  • Ensure the transparency of the transport cycle and grade of services.

The success of any logistics company depends on effective stock and warehousing management, automation of internal business processes, prompt delivery, and assuring the safe storage of goods. Data becomes helpful when it passes through this cycle. Wireless networks like Bluetooth, GSM, Wi-Fi, etc., offer information exchange in logistics processes.

IoT has now become part of all the sectors where transport is involved. That is, its impact and usage are just not limited to logistics and transport. Instead, it is used in manufacturing and retail trade, including e-commerce, hospitals, construction, and many other sectors. This enables transparency of processes in the supply chain, better and more stable work of transport and employees, and saves company resources.

The logistics business is attaining a new height after embracing IoT, as it provides efficacious solutions aimed at working with Big Data, speeding logistics supply chains, and many other things. This is supported by other advanced trends like the proliferation of the 5G Internet, the fast growth of mobile applications, and cloud services.

How to Prevent Data Lake from Turning into a Data Swamp?

IoT devices drive in many opportunities to gather more data than ever before. However, the challenge has changed; it is not about ways to get data but how to store an immense amount of data once it’s gathered. This is where data lakes come in the role. To clarify, a data lake is not just about a cheaper way to store data, but when it is appropriately crafted, data lakes act as a centralized source of truth that offers team members valuable flexibility to examine information that influences business decisions. This is only possible when we potentially utilize data lake practices. Raw data is like crude oil, requiring a thorough refinement process to distil more valuable products like gasoline. In the same way, raw data requires complex processing to get the most beneficial and business-rich insights to take action and measure outcomes.

With the increase in the volume of available data and the variety of its sources continuing to grow, many companies find themselves sitting on the data equivalent of a crude oil reservoir with no feasible way to extract the actual market worth. Traditional data warehouses are like gas stations; data lakes are oil refineries.

Data warehouses are becoming insufficient for managing the flooding business’s raw data. They need the information to be pre-processed like gasoline. Data lakes are the one that allows for the storage of both structured or unstructured data coming from different sources, such as business and mobile applications, IoT devices, social media etc.

Any idea? What does a well-maintained data lake look like? What is the best possible way to lead to implementation, and how do they impact the bottom line?

Explaining Data Lakes: How they Transform business

Data lakes are centralized storage entities to store any information mined to get actionable insights. These contain structured, unstructured, and other information from relational databases like text files, reports, videos, etc. A well-maintained data lake has real prospects to change the outlook of the business by offering a singular source for the company’s data regardless of its form and allowing business analysts and data science teams to extract information in a scalable and sustainable way. 

Data lakes are generally designed in a cloud-hosted environment like Microsoft Azure, Amazon Web Services or Google Cloud Platform. The vision offers compelling data practices that offer noticeable financial edges. These practices are approximately twenty times cheaper to access, store and analyze in a data lake rather than employing a traditional data warehouse. 

One of the reasons behind the domination of data lakes is the design structure or schema, which does not require to be written until after the data has been loaded. Regardless of the data’s format, the data remains as it is entered and does not separate into silos for different data sources. This automatically decreases the overall time for insight into an organization’s analytics. It also offers enhanced speed while accessing quality data that helps to inform business-critical activities. Advantages provided by data lakes like scalable architecture, cheaper storage and high-performance computing power allows companies to divert their shift from data collection to data processing in real-time. 

Rather than investing hours excavating scattered deposits, it provides one source to extract from that ultimately decreases dependency on human resources, which could be utilized to create stronger partnerships across teams. A data lakes give time to your data scientists to explore potential business-critical insights that could advise new business models in the future. 

Best Practices from the Experts

There are challenges in the data lakes process; it acts like a stagnant pool of water-polluting over time if it is not held to the correct standards. It becomes challenging to maintain and susceptible to flooding from insufficient data and poor design.

What to do to set up a supreme system for business transformation and growth?

Here we recommend the following actions to prevent your data lake from turning into a swamp.

Set Standards From the Start

A dynamic structure is the backbone of a healthy data lake. This means creating scalable and automated pipelines, using cloud resources for optimization, and monitoring connections and system performance. Initiate by making intentional data-design decisions during project planning. Mention standards and practices and ensure they are followed at each step in the implementation process. Meanwhile, allow your ecosystem to manage edge cases and the possibility for new data sources. Don’t forget; it is all about freeing up your data scientists from tending to an overtaxed data system so that they can shift their focus on other priority things.

Sustain Flexibility for Transformative Benefits

A healthy data lake exists in an environment that can manage dynamic inputs. This isn’t just about varying sources, sizes and types of data and how it is downed into storage.

For instance, creating an event-driven pipeline facilitates automation that offers source flexibility in file delivery schedules. Setting up a channel with trigger events for automation, based on when a file hits a storage location, eases concerns whenever the files come in. It is necessary to support the data science team’s fluidity around rapid testing, failing and learning to refine the analytics that empowers the company’s vital strategic endeavours, eventually driving unique, innovative opportunities.

Develop the System, Not the Processes

Most people have a misconception that problem-specific solutions may seem faster initially. One of the best things about data lakes is that they’re not connected or centralized around any one source. Hyper-specialized solutions for individual data sources restrict themselves to implementing change and need error management. Besides this, when a particular process is introduced, it doesn’t add value to the system as a whole as it cannot be utilized anywhere else.

Designing a data lake with modular processes and source-independent channels saves time in the long run by facilitating faster development time and streamlining the latest feature implementations.

Handle Standard Inventory to Find Opportunities

Event-driven pipelines are the best option for cloud automation, but the tradeoff demands post-event monitoring to comprehend what files are received and by whom and on which dates, etc.

One best way to monitor as well as share this information is to establish a summary dashboard of data reports from different sources. Adding alerting mechanisms for processing errors produces a notification when part of the data lake is not correctly functioning as expected. It even ensures that errors and exceptions are detected on time. When an immense amount of data is flooding, it becomes essential to track and handle it in the best possible way.

Right inventory initiatives create stable environments where data scientists feel supported in discovering additional metrics opportunities that can help make more robust business decisions in the future.

Revolutionize Business Intelligence

Data lake revolutionizes business intelligence by chartering a path for team members to peer clean data sources promptly and in the most effective way. A pristine data lake accelerates decision-making, removes struggle, and enhances business model ingenuity. So, we can conclude that prohibiting data lake getting muddied is necessary to get the optimal outcome. One must follow a few data lake practices that can reduce future headaches and keep your data streamlined and humming.

Big Data Analytics in IoT

What are the challenges with Big Data Analytics in IoT?

A successfully running IoT environment or system embodies interoperability, versatility, dependability, and effectiveness of the operation at a global level. Sift advancement and development in IoT is directly affecting data growth. Multiple networking sensors are continually collecting and carrying data (say geographical data, environment data, logistic data, astronomical data, etc.) for storage and processing operations in the cloud.

The initial devices involved in acquiring data in IoT are mobile devices, public facilities, transportation facilities and home appliances. The flooding of data suppresses the capabilities of IT architectures and infrastructure of enterprises. Besides this, the real-time analysis character considerably affects computing capability.

The generation of Big data by IoT has disturbed the current data processing ability of IoT and demands to adopt big data analytics to boost solutions’ capabilities. We can interpret that today success of IoT also depends on the potent association with big data analytics.

Big data is recommended for a thick set of heterogeneous data present in the unstructured, semi-structured and structured forms. Statista shares that big data revenue generates from service spending, representing almost 39 per cent of the total market as of 2019. In 2019, the data volume generated by IoT connected devices was around 13.6 zettabytes, and it might extend to 79 zettabytes by the end 0f 2025.

Big Data and IoT

Big data and IoT are two mind-blowing concepts, and both need each other for attaining ultimate success. Both endeavors to transform data into actionable insights.


Let’s take an example of an automatic milking machine developed using advanced technology like IoT and Big data.

AMCS
Source: Prompt Dairy Tech

Automatic milking machine software is designed by Prompt Softech. The Automatic Milk Collection Software (AMCS) is a comprehensive, multi-platform solution that digitizes the entire milk collection system. All the data is uploaded on the cloud, which provides real-time information on milk collection to the stakeholders.

AMCS enables transparency between dairy, milk collection centre and farmers. The shift from data filling on paper to digital data storage has reduced the chances of data loss along with human errors. A tremendous amount of data is processed and stored in the cloud daily. On the other hand, farmers get notified about the total amount of milk submitted and the other details. They can access the information about the payment and everything using the mobile app at any time.


This combination of real-time IoT insights and big-data analytics cuts off extra expenditure, improves efficacy and allows effective use of available resources.

Using Big Data:

Big data support IoT by providing easy functioning. Connected devices generate data, and it helps organizations in making business-oriented decisions.

Data processing includes the following steps:

  1. IoT connected devices generate a large amount of heterogeneous data stored in big data systems on a large scale. The data relies on the ‘Four “V” s of Big Data: Volume, Veracity, Variety & Velocity.
  2. A big data system is a shared and distributed system, which means that a considerable number of data records in big data files are present in the storage system.
  3. It uses an excellent analytic tool to analyze the data collected.
  4. It examines and produces a conclusion of the analyzed data for reliable and timely decision-making.

Challenges with Big Data Analytics

The key challenges associated with Big Data and IoT include the following:

Data Storage and Management:

The data generated from connected devices increases rapidly; however, most big data systems’ storage capacity is limited. Thus, it turns into a significant challenge to store and manage a large amount of data. Therefore, it has become necessary to develop frameworks or mechanisms to collect, save, and handle data.

Data Visualization:

Usually, data generated from connected devices are unstructured, semi-structured or structured in different formats. It becomes hard to visualize the data immediately. This implies preparing data for better visualization and understanding to get accurate decision-making in real-time while improving organizational efficiency.



Confidentiality and Privacy:

We all know that every IoT-enabled devices generate enormous data that requires complete data privacy and protection. The data collected and stored should stay confidential and have complete privacy as it contains users’ personal information.

Integrity:

Smart devices are specialists in sensing, communicating, information sharing, and carrying analysis for various applications. The device assures users of no data leakage and hijacking. Data assembly methods must use some measure and condition of integrity strongly with standard systems and commands.

Power Captivity:

Internet-enabled devices need a constant power supply for the endless and stable functioning of IoT operations. Many connected devices are lacking in terms of memory, processing power, and energy –– so they must adopt light-weighted mechanisms.

Device Security:

Analytics face device security challenges as big data are vulnerable to attacks. Data processing faces challenges due to short computational, networking, and storage at the IoT device.

Many Big Data tools provide valuable and real-time data to globally connected devices. Big data and IoT examine data precisely and efficiently using suitable techniques and mechanisms. Data analytics may differ with the types of data drawn from heterogeneous sources.


Source: IoTForAll – Challenges with Big Data Analytics in IoT

Adoption of Smart Cities for a Brighter Future

Adoption of Smart Cities for a Brighter Future

Are you living in a smart home? Just look around, and you’ll observe it’s a heck of a lot smarter than it was ten or even five years ago. Today our homes are occupied with smart devices, like smart speakers, smart thermostats, and smart lightbulbs. But the trend did not remain to the smart home, but the trend for intelligent spaces extends far beyond our homes. Now, everything is becoming a part of this trend, including smart cities. Smart cities are dependent on the latest technologies like 5G, IoT and fibre infrastructure. The latest innovations and changes in 5G, IoT and fibre infrastructure technologies are developing and strengthening the networks of the future.

Smart cities are utilizing these networks for smooth functioning. However, to ensure smart cities’ sustainability for the future, the connectivity infrastructure must be supported by highly efficient energy networks.

Let’s know everything in detail- what are smart cities, how technology is changing the outlook of cities, and what are the key investment opportunities?

Why do we need to remodel cities into smart ones?

A smart city uses advanced technologies to magnify efficiencies and enhance its residents’ quality of services and life. It usually embraces smart power distribution, transportation system, street lights and rubbish collection etc. The main concept behind the idea is to use data and technology smartly to make everyday life easier and better for people residing and working in the city while potentially utilizing the present resources.

Smart cities are built to enhance energy efficiency and help their regions reach their respective net-zero carbon emissions targets. It is expected that smart cities can generate around $20 trillion in economic benefits by 2026.

UN has predicted that 68 per cent of the world’s population would be living in urban areas by the end of 2050, which intimates that there would be more pressure on cities. There would be environmental, economic and societal challenges. The only solution to upcoming problems is by making cities smart.

By introducing smart cities, we can make cities a better and safe place to live. It can improve the fundamental quality of life indicators like daily commute, health issues and reduce crime incidents by around 10 to 30 per cent.

How is technology making cities smart?

By controlling traffic problems:

Traffic is a curse for everyone living in cities but thanks to technology as it has provided some promising solutions. For example, one can adjust the route in real-time according to demand, and smart traffic lights can be used to manage the congestion.

Potentially utilizing city resources:

There are sensors attached to the waste containers to report the container’s real-time status, which means that refuse collectors do not have to waste time travelling and collecting half-full refuse containers. This also implies that collectors can keep a check on how many bins are about to overflow and when they need to replace them. This facility is far better than measuring abstract factors like how many collection trucks are at work.

Enhancing energy efficiency:

Smart cities use technology to monitor real-time energy use and energy consumption closely. For example,

In Schenectady, New York, most street lights are upgraded to LED technology, allowing the lights to be adjusted based on real-time data. In Amsterdam, homes have smart energy meters designed to reduce energy consumption. This enables citizens to use energy sources consciously.

Making cities safer:

Today, the adoption of the latest technology has improved the safety and security system of the residents. Smart Cities utilize technology inventions like wifi, IoT and CCTV cameras to strengthen safety and improve incident response times. For example,

In many smart cities, real-time video data from the street is analyzed to better track and designate resources on the ground and enhance public safety.

Strengthening greater collaboration with citizens:

One of the best things about Smart cities is that it invites residents to participate in local issues. There are some apps that empower residents and allow them to report local problems. They can even share resources among themselves by using community networking platforms.

For example, Smart Cities have a low-cost environmental kit that is used to collect local ecological data. Residents place it in locations like balconies and windowsills to collect the local environment’s data like air, noise, and pollution. The collected data is then streamed to an online platform, efficiently creating a crowdsourced map of data worldwide.

What are the four infrastructure investment opportunities?

Empowering technologies:

The speedy growth of advanced technologies like 5G, Artificial Intelligence, cloud and edge computing supports smart cities’ evolution. We are currently in the starting stage of an edge computing revolution. It is absolutely essential to support the rapid growth in the number of connected devices and the massive increase in data gathered.

It is expected that approximately $20 billion of opportunities across hardware, software and services would be generated by the end of 2023.

It is clear that investment in reliable technology and high-speed connectivity is the central concept in building Smart City. The sudden shift to work from home culture in 2020 due to pandemic demands for stable and secure high-speed connectivity. With the increase in the number of connected infrastructures, cities must be aware of weaknesses and probable emerging problems.

Buildings and construction:

Climate change is one of the primary concern in today’s world. So decarbonizing the sector is the only possible and cost-effective ways to decrease the emerging climate change problem.

It is noted that most commercial buildings are responsible for 20% of energy use in the United States, in which 30% of energy is usually wasted. However, the addition of smart solutions can transform buildings into smart ones and make them energy-efficient and completely automated.

As per the Paris agreement, all buildings require to be net-zero carbon by 2050, and this goal increases the demand for smart buildings.

Updated energy sourcing, management and deployment:

Today, cities are consuming around two-thirds of the world’s energy; thus, there is immense pressure to shift to lower-carbon energy systems. No, doubt investment in smart technology will accelerate the shift while yielding economic growth and competition. Investment in next-generation energy transmission, smart grids and distribution networks can help monitor energy flows and adapt to fluctuations in supply and demand, respectively. Smart cities will also include smart meters to introduce price differentiation, microgrids for a local energy source, gamification apps to lower consumer usage etc.

Smart water and waste management solution:

With climate changes, water management and waste management are another concern to be resolved as soon as possible. Today, access to potable water, treatment of wastewater and waste management are essential issues that cities are facing. Besides this, flooding, drought and water losses are also growing threats due to climate changes and urbanization. Urban planners have to keep track of the ageing drainage systems.

Therefore the introduction of smart solutions has become necessary to overcome water-related issues. The addition of a smart water management system will include leakage, pollution detection and predictive maintenance planning.

For waste management, there are sensors attached to the bins that update the refusal bin’s status. Refusal collectors keep track of the data and collect the waste containers accordingly. This minimizes the travelling time and cuts off the fuel charges. A smart waste management system also stresses reducing waste at the source through advanced use of packaging, strategic collection methods and divided waste-to-energy solutions.

Conclusion:

Smart cities are the best solution to tackle environmental, social and safety issues. It includes everything which is required for the betterment of lifestyle. From smart homes to street lights, smart meter to smart traffic lights, everything contributes to saving natural resources and potentially utilizing available resources. Prompt Softech is dedicatedly working on converting the traditional technology and working process into smart ones. The Softech company provides IoT enabled solutions that can bring changes to your organization by making processes and operations more effective and efficient, ultimately leading to optimal outcome, better experience and the slightest error. You can initiate the ‘smart city’ revolution by adding smart solutions to your organization and cut off unnecessary expenditure.

How China, US and Taiwan used Big Data In The Fight Against Coronavirus?

Big data is no new word in today’s tech wrapped world. Today in this corona crisis situation, Big data has emerged as an incomparable tech invention for different purposes.

It is used in the fight against CoronaVirus, thus suggesting the need for the further development of big data and requirement of Big Data analysis for different purposes.

There are many Big data specialist companies delivering Big Data solutions for better approaches to the result.

Countries are tapping into Big data, Internet Of Things and Machine learning to track and identify the outbreak. They are using digital technology to get real-time forecasts and help healthcare professionals and government for predicting the impact of the COVID-19.

Let’s switch back to the real topic we were talking about. Let’s know about the vital role played by Big Data in this COVID-19 fight.

Surveillance Infrastructure In China:

The first in the list is China as it is the place where COVID-19 first case was reported. China’s monitoring culture emerged as a powerful weapon in the fight against COVID-19. China installed thermal scanners in train stations to detect the body temperature and separate the probably infected one. As we know, high fever is the symptom of COVID-19; the passengers showing the symptom were stopped by health officials to undergo coronavirus testing. If the test comes positive, then the administration would alert all other passengers who might have exposure to the virus so that they could follow self-quarantine.

China has installed millions of security camera to keep a track over the citizens’ activities and curb the crime rates. These cameras were used to discover people who were not following the proposed quarantine to stop the spread of the virus.

If a person who was supposed to be in quarantine, but cameras tracked them outside their homes, authorities were called to take appropriate actions.

In fact, the Chinese government also used an app named “Close Contact Detector” that notified users if they had contact with someone who was corona virus-positive.

Travel verification reports/data shared by telecom providers were used to list all the cities visited by the user in the last 14 days to check whether quarantine was recommended based on their location or not.

The integration of data collected by using the surveillance system helped the country in exploring the ways to curb the spread of the coronavirus.

Read More: Will 2020 Be The Transition Phase of Internet Of Things?

Big Data Analytics and Taiwan’s successful pandemic strategy:

After the observation of painful stage in China because of Corona spread, it was expected that Taiwan would be hit harder than China.

But, surprisingly, Taiwan faced the virus havoc very smartly. It used advanced technology and strong pandemic plan, which they prepared after the 2003 SARS outbreak to control the virus’s impact there.

Taiwan has integrated its national health insurance database with migration and custom database. Through this centralisation of data, the country faced the coronavirus strongly. They got real-time alerts regarding the probably infected one on the basis of symptoms & their travel history.

The country even had a QR code scanning and online reporting of travel and health symptoms that helped the medical officials to categorise the travellers’ infection risk. They even provided a toll-free hotline for citizens to report symptoms.

When the first corona case was reported, and WHO informed about the pneumonia of unknown cause in China, Taiwan activated all its warriors, including technology. This quick and active response taken by the country saved it from the severe effect of this fatal disease.

Use of Mobile Apps in Pandemic:

In America and Europe, people’s privacy is the priority still medical researchers and bioethics focused on the power of technology and supported its use for contact tracing in a pandemic.

Oxford University’s Big Data Institute co-operated with government officials to explain the advantages of a mobile app that provides valuable data for controlling coronavirus spread.

As we know, mostly coronavirus transmissions occur before the symptoms are visible thus speed and effectiveness to alarm people had been deemed as supreme during a pandemic like a coronavirus.

Read More: How can IoT be Used to Fight Against COVID-19?

A mobile app that holds the advanced 21st-century technology can help in the notification process while maintaining principles to decelerate the infection spread rate.

In 2011, Tech experts had developed a solution to monitor and track the spread of flu efficiently, but the app wasn’t adopted, thus limited its usage.

Now organisations are working to develop app solutions that can provide a platform where people can self-identify their health status and symptoms.

For the development of such apps, there are many app development companies that offer the most advanced and reliable services.

Corona has not just given us health challenges but also providing necessary learning experiences for data science in healthcare.

In US, the government is conversing with tech hulks like google, Facebook, and many others to know the possibilities for using location data from smartphones to track the movements of its citizens and analyse the patterns.

Dashboard to track the virus spread:

Dashboard is another tool that has been proved helpful for citizens, healthcare workers, and government policymakers to see the progression of contagion and how invasive this virus would be.

Dashboard collects the data from around the world to display the no. of confirmed cases and deaths caused by coronavirus with locations.

This data can be analysed and used to create models and find the existing hotspot of the disease, which could help in proper decision making for the home-quarantine period and help healthcare systems to prepare for the coming challenges.

Outbreak analytics use all the available data like confirmed cases, infected people, deaths, map, population densities, traveller flow etc. and then process it using machine learning for the development of possible patterns of the disease. These models are further used to get the best predictions of the infection rates and results.

Thus, it is obvious that proper use of big data solutions and big data analysis can help countries in this pandemic. Big data, machine learning and other technologies can provide a model and predict the flow of a pandemic. It can analyse data to assist the health officials in preparation for the fight against Corona or any other future pandemics.

How to Overcome Looming Threats on Big Data?

The continuation in the advancement of technology generates approximately 2.5 quintillion bytes of data daily. Protection of the data is a vital responsibility of the service provider company. Data security has now erupted as a big concern and must be guarded in proficient ways.

Big data security- this term aggregates all the measures and tools which are used to safeguard both data available cloud and on-premise from malicious activities, attacks, or any thefts that could compromise their confidentiality.

Data is vulnerable:

The increase in the amount of data is directly proportional to threats like DDoS attacks, information piracy, ransomware etc. These attacks could turn even worse when companies store sensitive and confidential information like Contact Info, Identity Information, Credit Card numbers, Bank Details etc. Additionally, attacks on a provider company’s big data could cause severe financial repercussions such as losses, litigation costs, and fines or sanctions. In fact, an unauthorized user might get access and misuse your big data and sell valuable information.

Today most of the data incoming/outgoing pass through Web API. In part, we are sharing some key errors by developers that could lead an organization to significant risk and some simple techniques to mitigate this risk.

Web API mistakes:

The common mistakes made in Web API are listed below:

  • Simple authentication like username/password
  • Weak token encryption
  • Sensitive information like token, username, a password is stored in plain text in cookies
  • No data authorization. Any authorized user has access to all data

Also Read: How To Improve Web Application Security?

Best Practices to tighten the Big data Security:

A) Authentication

API security is complex and requires an explicit crystal knowledge. Many times you built an API that you want to the public, but at the same time, you do not want everybody to access it. In such cases, you need to have control over who can access the API.

For the same reason, use strong authentication like OAuth 2.0, token using HMAC Algorithm or SHA256 with an expiry date. For an additional security use whitelist source IP Address. This would avoid requests for a given token from unauthorized IPs.

Always keep a log of all authentication request with DateTime stamp and source IP.

B) Authorization

The authorization permits a user to grant or restrict permissions on functionality and data. Always implement authorization at Logic or Database Layer. i.e. each request must be authorized of ‘which data access’ is allowed.

C) Brute force

A brute force attack is a trial and approach type cyber attack with a purpose to crack a password or username or find a hidden web page or find the key used to encrypt a message. This method being old is still active and popular among hackers. In this attack attempt, attackers try different user-names and passwords or tokens.

To stop such attacks write codes in a way to auto-detect brute force attack or slow down a client if it makes the number of requests often or directly block such IP for some time.

D) Cookies

Cookies are stored in the browser cache and are easily readable. Never leave sensitive data in cookies as plain text. Use secure encryption like AES 256 with unidentifiable cookie name. Like ‘token’ can be named as ‘zeta’.

E) Set Limits

Set request limits per minute/hour. Configure alerts for flooded request from the same IP.

These are certainly best practices to endeavour the best cybersecurity and nullify the looming attacks.