6 posts tagged with "ml"

Can an AI-based edge computing platform drive businesses or is that a myth? We explore this topic here._

Introduction#

For a long time, artificial intelligence has been a hot issue. We've all heard successful tales of forward-thinking corporations creating one brilliant technique or another to use Artificial Intelligence technology or organizations that promise to put AI-first or be truly "AI-driven." For a few years now, Artificial Intelligence (AI) has been impacting sectors all around the world. Businesses that surpass their rivals are certainly employing AI to assist in guiding their marketing decisions, even if it isn't always visible to the human eye (Davenport et al., 2019). Machine learning methods enable AI to be characterized as machines or processes with human-like intelligence. One of the most appealing features of AI is that it may be used in any sector. By evaluating and exploiting excellent data, AI can solve problems and boost business efficiency regardless of the size of a company (Eitel-Porter, 2020). Companies are no longer demanding to be at the forefront or even second in their sectors; instead, businesses are approaching this transition as if it were a natural progression.

Artificial Intelligence's (AI-driven) Business Benefits#

Businesses had to depend on analytics researchers in the past to evaluate their data and spot patterns. It was practically difficult for them to notice each pattern or useful bit of data due to the huge volume of data accessible and the brief period in their shift. Data may now be evaluated and processed in real-time thanks to artificial intelligence. As a result, businesses can speed up the optimization process when it comes to business decisions, resulting in better results in less time. These effects can range from little improvements in internal corporate procedures to major improvements in traffic efficiency in large cities (Abduljabbar et al., 2019). The list of AI's additional advantages is nearly endless. Let's have a look at how businesses can benefit:

• A More Positive Customer Experience: Among the most significant advantages of AI is the improved customer experience it provides. Artificial intelligence helps businesses to improve their current products by analyzing customer behavior systematically and continuously. AI can also help engage customers by providing more appropriate advertisements and product suggestions (Palaiogeorgou et al., 2021).

• Boost Your Company's Efficiency: The capacity to automate corporate procedures is another advantage of artificial intelligence. Instead of wasting labor hours by having a person execute repeated activities, you may utilize an AI-based solution to complete those duties instantly. Furthermore, by utilizing machine learning technologies, the program can instantly suggest enhancements for both on-premise and cloud-based business processes (Daugherty, 2018). This leads to time and financial savings due to increased productivity and, in many cases, more accurate work.

• Boost Data Security: The fraud and threat security capabilities that AI can provide to businesses are a major bonus. AI displays usage patterns that can help to recognize cyber security risks, both externally and internally. An AI-based security solution could analyze when specific employees log into a cloud solution, which device they used, and from where they accessed cloud data regularly.

Speaking with AI Pioneers and Newcomers#

Surprisingly, by reaching out on a larger scale, researchers were able to identify a variety of firms at various stages of AI maturity. Researchers split everyone into three groups: AI leaders, AI followers, and AI beginners (Brock and von Wangenheim, 2019). The AI leaders have completely adopted AI and data analysis tools in their company, whilst the AI beginners are just getting started. The road to becoming AI-powered is paved with obstacles that might impede any development. In sum, 99% of the survey respondents have encountered difficulties with AI implementation. And it appears that the more we work at it, the more difficult it becomes. 75% or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more effort than the other two groups and began 4-5 years earlier, had over 60% of their projects encounter difficulties. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. The staff was resistant to embracing new methods of working or were afraid of losing their employment. Considering this, it should be unsurprising that the most important tactics for overcoming obstacles include culture and traditions (Campbell et al., 2019). Overall, it's evident that the transition to AI-driven operations is a cultural one!

The Long-Term Strategic Incentive to Invest#

Most firms that begin on an organizational improvement foresee moving from one stable condition to a new stable one after a period of controlled turbulence (ideally). When developers look at how these AI-adopting companies envision the future, however, this does not appear to be the case. Developers should concentrate their efforts on the AI leaders to better grasp what it will be like to be entirely AI-driven since these are the individuals who've already progressed the most and may have a better understanding of where they're headed. It's reasonable to anticipate AI leaders to continue to outpace rival firms in the future (Daugherty, 2018). Maybe it's because they have a different perspective on the current, solid reality that is forming. The vision that AI leaders envisage is not one of consistency and "doneness" in terms of process. Consider a forthcoming business wherein new programs are always being developed, with the ability to increase efficiency, modify job processing tasks, impact judgment, and offer novel issue resolution. It appears that the steady state developers are looking for will be one of constant evolution. An organization in which AI implementation will never be finished. And it is for this reason that we must start preparing for AI Edge Computing Platform to pave the way for the future.

References#

• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), p.189. Available at: link.
• Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), pp.110–134.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2019). From Data to Action: How Marketers Can Leverage AI. Business Horizons.
• Daugherty, P.R. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), pp.24–42. Available at: link.
• Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics.
• Palaiogeorgou, P., Gizelis, C.A., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., & Christonasis, A.M. (2021). AI: Opportunities and Challenges - The Optimal Exploitation of (Telecom) Corporate Data. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.47–59.

Learn more about Artificial Intelligence - AI in the workforce in this article.

Introduction#

An increase in data usage demands a network effectiveness strategy, with a primary focus on lowering overall costs. The sophistication of networks is expanding all the time. The arrival of 5G on top of existing 2G, 3G, and 4G networks, along with customers' growing demands for a user platform comparable to fibre internet, places immense strain on telecommunication operators handling day-to-day activities (Mishra, 2018). Network operators are also facing significant financial issues as a result of declining revenue per gigabyte and market share, making maximizing the impact on network investment strategies vital for existence.

How can businesses use AI to change the way businesses make network financial decisions?#

From sluggish and labor-intensive to quick, scalable, and adaptable decisions - The traditional manual planning method necessitates a significant investment of both money and time. Months of labor-intensive operations such as data gathering, aggregation of data, prediction, prompting, proportioning, and prioritizing are required for a typical medium-sized system of 10,000 nodes. Each cell is simulated separately using machine learning, depending on its special properties. Several Key performance indicators are used in multivariable modeling approaches to estimate the efficiency per unit separately. By combining diverse planning inputs into the application, operators may examine alternative possibilities due to the significant reduction in turnaround time (Raei, 2017).

Moving from a network-centric to a user-centric approach - Basic guidelines are commonly used to compare usage to bandwidth. Customer bandwidth is influenced by some parameters, including resource consumption, such as DLPRB utilization. Individual unit KPI analysis utilizing machine learning solves this inefficacy, with the major two processes involved being traffic prediction and KPI predictions. The Key performance indicator model is a useful part of cognitive planning that is specific to each cell and is trained every day using the most up-to-date data. The per-cell model's gradient and angles are governed by its unique properties, which are impacted by bandwidth, workload, broadcast strength, as well as other factors (Kibria et al., 2018). This strategy provides more granularity and precision in predicting each cell's KPI and effectiveness.

From one-dimensional to two-dimensional to three-dimensional - Availability and efficiency are frequently studied in a one-dimensional manner, with one-to-one mappings of assets such as PRB to quality and productivity. Nevertheless, additional crucial elements such as broadcast frequency or workload have a significant impact on cell quality and productivity. Optimal TCO necessitates a new method of capacity evaluation that guarantees the correct solution is implemented for each challenge (Pahlavan, 2021).

Candidate selection for improvement - Units with poor wireless reliability and effectiveness are highlighted as candidates for improvement rather than growth using additional parameters such as radio quality (CQI) and spectrum efficiency in cognitive planning. As a first resort, optimization operations can be used to solve low radio-quality cells to increase network capacity and performance. Instead of investing CAPEX in hardware expansion, cognitive planning finds low radio-quality cells where capacity may be enhanced through optimization (Athanasiadou et al., 2019).

Candidate selection for load-balancing#

Before advocating capacity increase, cognitive planning tools will always model load-balancing among co-sector operators. This is done to eliminate any potential for load-balancing-related benefits before investing. The load-balancing impact is modeled using the machine-learning-trained KPI model by assuming traffic shifts from one operator to another and then forecasting the efficiency of all operators even in the same section (He et al., 2016). If the expected performance after the test does not satisfy the defined experience requirements, an extension is suggested; alternatively, the program generates a list of suggested units for load-balancing.

Prioritization's worth for AI in the workforce#

When network operators are hesitant to spend CAPEX, a strong prioritizing technique is vital to maximizing the return on investment (ROI) while guaranteeing that even the most relevant aspects are handled. This goal is jeopardized by outdated approaches, which struggle to determine the appropriate response and have the versatility to gather all important indicators. In the case of network modeling, load corresponds to the number of consumers, utilization (DLPRB utilization) to the space occupancy levels, and quality (CQI) to the size (Maksymyuk, Brych and Masyuk, 2015). The amount of RRC users, which is near to demand as a priority measure, is put into the prioritizing procedure, taking into account the leftover areas. Further priority levels are adjusted based on cell bandwidth, resulting in a more realistic order.

Developers give ideal suggestions and growth flow (e.g. efficiency and load rebalancing ahead of growth) and generate actual value by combining all of these elements, as opposed to the conventional way, which involves a full examination of months of field data:

• Optimization activities are used as a first option wherever possible, resulting in a 25% reduction in carrier and site expansions.

• When compared to crowded cells detected by operators, congested cells found by cognitive planning had a greater user and traffic density, with an average of 21% more RRC users per cell and 19% more data volume per cell. As a result, the return on investment from the capacity increase is maximized (Pahlavan, 2021).

• Three months before the experience objective was missed, >75 percent of the field-verified accuracy in determining which cells to grow when was achieved.

• Reduce churn

Conclusion for AI in the workforce#

The radio access network (RAN) is a major component of a customer service provider's (CSP) entire mobile phone network infrastructural development, contributing to around 20% of a cellular manufacturer's capital expenditures (CapEx). According to the findings, carriers with superior connection speeds have greater average revenue per user (+31%) and lower overall turnover (-27 percent) (Mishra, 2018). As highlighted in this blog, using Machine learning and artificial intelligence for capacity management is critical for making intelligent network financial decisions that optimize total cost of ownership (TCO) while offering the highest return in terms of service quality: a critical pillar for customer service provider's (CSP) commercial viability.

Learn more about Nife to be informed about Edge Computing and its usage in different fields https://docs.nife.io/blog

Introduction#

Machine Learning-Based Techniques for observation and administration are especially suitable for sophisticated network infrastructure operations. Assume a machine learning (ML) program designed to predict mobile service disruptions. Whenever a network administrator obtains an alert about a possible imminent interruption, they can take bold measures to address bad behaviour before something affects users. The machine learning group, which constructs the underlying data processors that receive raw flows of network performance measurements and store them into such a Machine Learning (ML)-optimized databases, assisted in the development of the platform. The preliminary data analysis, feature engineering, Machine Learning (ML) modeling, and hyperparameter tuning are all done by the research team. They collaborate to build a Machine Learning (ML) service that is ready for deployment (Chen et al., 2020). Customers are satisfied because forecasts are made with the anticipated reliability, network operators can promptly repair network faults, and forecasts are produced with the anticipated precision.

What is Machine Learning (ML) Lifecycle?#

The data analyst and database administrators obtain multiple procedures (Pipeline growth, Training stage, and Inference stage) to establish, prepare, and start serving the designs using the massive amounts of data that are engaged in different apps so that the organisation can take full favor of artificial intelligence and Machine Learning (ML) methodologies to generate functional value creation (Ashmore, Calinescu and Paterson, 2021).

Monitoring allows us to understand performance concerns#

Machine Learning (ML) models are based on numbers, and they tacitly presume that the learning and interpretation data have the same probability model. Basic variables of a Machine Learning (ML) model are tuned during learning to maximise predicted efficiency on the training sample. As a result, Machine Learning (ML) models' efficiency may be sub-optimal when compared to databases with diverse properties. It is common for data ranges to alter over time considering the dynamic environment in which Machine Learning (ML) models work. This transition in cellular networks might take weeks to mature as new facility units are constructed and updated (Polyzotis et al., 2018). The datasets that ML models consume from multiple data sources and data warehouses, which are frequently developed and managed by other groups, must be regularly watched for unanticipated issues that might affect ML model results. Additionally, meaningful records of input and model versions are required to guarantee that faults may be rapidly detected and remedied.

Data monitoring can help prevent machine learning errors#

Machine Learning (ML) models have stringent data format requirements because they rely on input data. Whenever new postal codes are discovered, a model trained on data sets, such as a collection of postcodes, may not give valid forecasts. Likewise, if the source data is provided in Fahrenheit, a model trained on temperature readings in Celsius may generate inaccurate forecasts (Yang et al., 2021). These small data changes typically go unnoticed, resulting in performance loss. As a result, extra ML-specific model verification is recommended.

Variations between probability models are measured#

The steady divergence between the learning and interpretation data sets, known as idea drift, is a typical cause of efficiency degradation. This might manifest itself as a change in the mean and standard deviation of quantitative characteristics. As an area grows more crowded, the frequency of login attempts to a base transceiver station may rise. The Kolmogorov-Smirnov (KS) test is used to determine if two probability ranges are equivalent (Chen et al., 2020).

Preventing Machine Learning-Based Techniques for system engineering problems#

The danger of ML efficiency deterioration might be reduced by developing a machine learning system that specifically integrates data management and model quantitative measurement tools. Tasks including data management and [ML-specific verification] are performed at the data pipeline stage. To help with these duties, the programming group has created several public data information version control solutions. Activities for monitoring and enrolling multiple variations of ML models, as well as the facilities for having to serve them to end-users, are found at the ML model phase (Souza et al., 2019). Such activities are all part of a bigger computer science facility that includes automation supervisors, docker containers tools, VMs, as well as other cloud management software.

Data and machine learning models versioning and tracking for Machine Learning-Based Techniques#

The corporate data pipelines can be diverse and tedious, with separate elements controlled by multiple teams, each with their objectives and commitments, accurate data versioning and traceability are critical for quick debugging and root cause investigation (Jennings, Wu and Terpenny, 2016). If sudden events to data schemas, unusual variations to function production, or failures in intermediate feature transition phases are causing ML quality issues, past and present records can help pin down when the problem first showed up, what data is impacted, or which implication outcomes it may have affected.

Using current infrastructure to integrate machine learning systems#

Ultimately, the machine learning system must be adequately incorporated into the current technological framework and corporate environment. To achieve high reliability and resilience, ML-oriented datasets and content providers may need to be set up for ML-optimized inquiries, and load-managing tools may be required. Microservice frameworks, based on containers and virtual machines, are increasingly widely used to run machine learning models (Ashmore, Calinescu, and Paterson, 2021).

Conclusion for Machine Learning-Based Techniques#

The use of Machine Learning-Based Techniques could be quite common in future communication designs. At this scale, vast amounts of data streams might be recorded and stored, and traditional techniques for assessing better data and dispersion drift could become operationally inefficient. The fundamental techniques and procedures may need to be changed. Moreover, future designs are anticipated to see an expansion in the transfer of computing away from a central approach and onto the edge, closer to the final users (Hwang, Kesselheim and Vokinger, 2019). Decreased lags and Netflow are achieved at the expense of a more complicated framework that introduces new technical problems and issues. In such cases, based on regional federal regulations, data gathering and sharing may be restricted, demanding more cautious ways to programs that prepare ML models in a safe, distributed way.

References#

• Ashmore, R., Calinescu, R. and Paterson, C. (2021). Assuring the Machine Learning Lifecycle. ACM Computing Surveys, 54(5), pp.1–39.
• Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S.A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., Zaharia, M., Zang, R., Zheng, J. and Zumar, C. (2020). Developments in MLflow. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning.
• Hwang, T.J., Kesselheim, A.S. and Vokinger, K.N. (2019). Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine. JAMA, 322(23), p.2285.
• Jennings, C., Wu, D. and Terpenny, J. (2016). Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6(9), pp.1428–1439.
• Polyzotis, N., Roy, S., Whang, S.E. and Zinkevich, M. (2018). Data Lifecycle Challenges in Production Machine Learning. ACM SIGMOD Record, 47(2), pp.17–28.
• Souza, R., Azevedo, L., Lourenco, V., Soares, E., Thiago, R., Brandao, R., Civitarese, D., Brazil, E., Moreno, M., Valduriez, P., Mattoso, M., Cerqueira, R. and Netto, M.A.S. (2019).
• Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. 2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS).
• Yang, C., Wang, W., Zhang, Y., Zhang, Z., Shen, L., Li, Y. and See, J. (2021). MLife: a lite framework for machine learning lifecycle initialization. Machine Learning.

Are you familiar with the differences between edge computing and cloud computing? Is edge computing a type of branding for a cloud computing resource, or is it something new altogether? Let us find out!

The speed with which data is being added to the cloud is immense. This is because the growing number of devices in the cloud are centralized, so it must transact the information from where the cloud servers are, hence data needs to travel from one location to another so the speed of data travel is slow. If this transaction starts locally, then the data travels at a shorter distance, making it faster. Therefore, cloud suppliers have combined Internet of Things strategies and technology stacks with edge computing for the best usage and efficiency.

In the following article, we will understand the differences between cloud and edge computing. Let us see what this is and how this technology works.

EDGE COMPUTING#

Edge Computing is a varied approach to the cloud. It is the processing of real-time data close to the data source at the edge of any network. This means applications close to the data generated instead of processing all data in a centralized cloud or a data center. It increases efficiency and decreases cost. It brings the storage and power closer to the device where it is most needed. This distribution eliminates lag and saves a scope for various other operations.

It is a networking system, within which data servers and data processing are closer to the computing process so that the latency and bandwidth problems can be reduced.

Now that we know what the basics of edge computing are, let's dive in a little deeper for a better understanding of terms commonly associated with edge computing:

Latency#

Latency is the delay in contacting in real-time from a remotely located data center or cloud. If you are loading an image over the internet, the time to show up completely is called the latency time.

Bandwidth#

The frequency of the maximum amount of data sent over an Internet connection at a time is called Bandwidth. We refer to the speed of sent and received data over a network that is calculated in megabits per second or MBPS as bandwidth.

Leaving latency and bandwidth aside, we choose edge computing over cloud computing in hard-to-reach locations, where there is limited or no connectivity to a central unit or location. These remote locations need local computing, and edge computing provides the perfect solution for it.

Edge computing also benefits from specialized and altered device functions. While these devices are like personal computers, they are not regular computing devices and perform multiple functions benefiting the edge platform. These specialized computing devices are intelligent and respond to machines specifically.

Benefits of Edge Computing#

• Gathering data, analyzing, and processing is done locally on host devices on the edge of the network, which has the caliber to be completed within a fraction of a second.

• It brings analytical capabilities comparatively closer to the user devices and enhances the overall performance.

• Edge computing is a cheaper alternative to the cloud as data transfer is a lengthy and expensive process. It also decreases the risk involved in transferring sensitive user information.

• Increased use of edge computing methods has transformed the use of artificial intelligence in autonomous driving. Artificial Intelligence-powered and self-driving cars and other vehicles require massive data presets from their surroundings to function perfectly in time. If we use cloud computing in such a case, it would be a dangerous application because of the lag.

• The majority of OTT platforms and streaming service providers like Netflix, Amazon Prime, Hulu, and Disney+ to name a few, create a heavy load on cloud network infrastructure. When popular content is cached closer to the end-users in storage facilities for easier and quicker access. These companies make use of the nearby storage units close to the end-user to deliver and stream content with no lag if one has a stable network connection.

The process of edge computing varies from cloud computing as the latter takes considerably more time. Sometimes it takes up to a couple of seconds to channel the information to the data centers, ultimately resulting in delays in crucial decision-making. The signal latency can translate to huge losses for any organization. So, organizations prefer edge computing to cloud computing which eliminates the latency issue and results in the tasks being completed in fractions of a second.

CLOUD COMPUTING#

A cloud is an information technology environment that abstracts, pools, and shares its resources across a network of devices. Cloud computing revolves around centralized servers stored in data centers in large numbers to fulfill the ever-increasing demand for cloud storage. Once user data is created on an end device, its data travels to the centralized server for further processing. It becomes tiresome for processes that require intensive computations repeatedly, as higher latency hinders the experience.

Benefits of Cloud Computing#

• Cloud computing gives companies the option to start with small clouds and increase in size rapidly and efficiently as needed.

• The more cloud-based resources a company has, the more reliable its data backup becomes, as the cloud infrastructure can be replicated in case of any mishap.

• There is little to no service cost involved with cloud computing as the service providers conduct system maintenance on their own from time to time.

• Cloud enables companies to help cut expenses in operational activities and enables mobile accessibility and user engagement framework to a higher degree.

• Many mainstream technology companies have benefited from cloud computing as a resourceful platform. Slack, an American cloud-based software as a service, has hugely benefited from adopting cloud servers for its application of business-to-business and business-to-consumer commerce solutions.

• Another largely known technology giant, Microsoft has its subscription-based product line ‘Microsoft 365' which is centrally based on cloud servers that provide easy access to its office suite.

• Dropbox, infrastructure as a service provider, provides a service- cloud-based storage and sharing system that runs solely on cloud-based servers, combined with an online-only application.

KEY DIFFERENCES#

• The main difference between edge computing and cloud computing is in data processing within the case of cloud computing, data travel is long, which causes data processing to be slower but in contrast edge computing reduces the time difference in the data processing. It's essential to have a thorough understanding of the working of cloud and edge computing.

• Edge computing is based on processing sensitive information and data, while cloud computing processes data that is not time constrained and uses a lesser storage value. To carry out this type of hybrid solution that involves both edge and cloud computing, identifying one's needs and comparing them against monetary values must be the first step in assessing what works best for you. These computing methods vary completely and comprise technological advances unique to each type and cannot replace each other.

• The centralized locations for edge computing need local storage, like a mini data center. Whereas, in the case of cloud computing, the data can be stored in one location. Even when used as part of manufacturing, processing, or shipping operations, it is hard to co-exist without IoT. This is because everyday physical objects that collect and transfer data or dictate actions like controlling switches, locks, motors, or robots are the sources and destinations that edge devices process and activate without depending upon a centralized cloud.

With the Internet of Things gaining popularity and pace, more processing power and data resources are being generated on computer networks. Such data generated by IoT platforms is transferred to the network server, which is set up in a centralized location.

The big data applications that benefit from aggregating data from everywhere and running it through analytics and machine learning to prove to be economically efficient, and hyper-scale data centers will stay in the cloud. We chose edge computing over cloud computing in hard-to-reach locations, where there is limited connectivity to a cloud-based centralized location setup.

CONCLUSION#

The edge computing and cloud computing issue does not conclude that deducing one is better than the other. Edge computing fills the gaps and provides solutions that cloud computing does not have the technological advancements to conduct. When there is a need to retrieve chunks of data and resource-consuming applications need a real-time and effective solution, edge computing offers greater flexibility and brings the data closer to the end user. This enables the creation of a faster, more reliable, and much more efficient computing solution.

Therefore, both edge computing and cloud computing complement each other in providing an effective response system that is foolproof and has no disruptions. Both computing methods work efficiently and in certain applications, edge computing fills and fixes the shortcomings of cloud computing with high latency, fast performance, data privacy, and geographical flexibility of operations.

Functions that are best managed by computing between the end-user devices and local networks are managed by the edge, while the data applications benefit from outsourcing data from everywhere and processing it through AI and ML algorithms. The system architects who have learned to use all these options together have the best advantage of the overall system of edge computing and cloud computing.

Learn more about different use cases on edge computing-

Condition-based monitoring - An Asset to equipment manufacturers (nife.io)

AR Mirrors or Smart mirrors, the future of mirrors, is known as the world's most advanced Digital Mirrors. Augmented Reality mirrors are a reality today, and they hold certain advantages amidst COVID-19 as well.

Learn More about how to deploy and scale Smart Mirrors.

Introduction#

AR Mirrors are the future and are used in many places for ease of use for the end-users. AR mirrors are also used in Media & Entertainment sectors because the customers get easy usage of these mirrors, the real mirrors. The AI improves the edge's performance, and the battery concern is eradicated with edge computing.

Background#

Augmented Reality, Artificial intelligence, Virtual reality and Edge computing will help to make retail stores more interactive and the online experience more real-life, elevating the customer experience and driving sales.

Recently, in retail markets, the use of AR mirrors has emerged, offering many advantages. The benefits of using these mirrors are endless, and so is the ability of the edge.

For shoppers to go back to the stores, the touch and feel are the last to focus on. Smart Mirrors bring altogether a new experience of visualizing different garments, how the clothes actually fit on the person, exploring multiple choices and sizes to create a very realistic augmented reflection, yet avoiding physical wear and touch.

About#

We use real mirrors in trial rooms to try clothes and accessories. Smart mirrors have become necessary with the spread of the pandemic.

The mirrors make the virtual objects tangible and handy, which provides maximum utility to the users building on customer experience. Generally, as human nature, the normal mirrors in the real world more often to get a look and feel.

Hence, these mirrors take you to the virtual world, help you with looking at jewellery, accessories and even clothes making the shopping experience more holistic.

Smart Mirrors use an embedded processor with AI. The local processor ensures no lag when the user is using the Mirrors and hence provides an inference closest to the user. While this helps with the inference, the cost of the processor increases.

In order to drive large scale deployment, the cost of mirrors needs to be brought down. Today, AR mirrors have a high price, hence deploying them in retail stores or malls has become a challenge.

The other challenge includes updates to the AR application itself. Today, the System Integrator needs to go to every single location and update the application.

Nife.io delivers by using minimum unit architecture, each connected to the central edge server that can lower the overall cost and help to scale the application on Smart Mirror

Key challenges and drivers of AR Mirrors#

• Localized Data Processing
• Reliability
• Application performance is not compromised
• Reduced Backhaul

Result#

AR Mirrors deliver a seamless user experience with AI. It is a light device that also provides data localization for ease of access to the end-user.

AR Mirrors come with flexible features and can easily be used according to the user's preference.

Here, edge computing helps in reducing hardware costs and ensures that the customers and their end-users do not have to compromise with application performance.

1. The local AI processing moves to the central server.
2. The processor now gets connected to a camera to get the visual information and pass it on to the server.

Since the processing is moved away from the server itself, this helps AR mirrors also can help reduce battery life.

The critical piece here is lag in operations. The end-user should not face any lag, the central server then must have enough processing power and enough isolations to run the operations.

Since the central server with network connectivity is in the control of the application owner and the system integrator, the time spent to deploy in multiple servers is completely reduced.

How does Nife Help with AR Mirrors?#

Use Nife to offload device compute and deploy applications close to the Smart Mirrors.

• Offload local computation
• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Smart Mirrors (40% Cost Reduction)
• Manage and Monitor all applications in a single pane of glass.
• Seamlessly deploy and manage applications ( 5 min to deploy, 3 min to scale)

What's happening with 5G and the 5G networks innovation and rollout? How are these shaping innovation and the world we know? Are you curious? Read More!

We will never forget the year 2020 as the year of the COVID-19 pandemic. We all remember how we witnessed a lengthy lockdown during 2020, and it put all our work on a halt for some time. But we all know that the internet remains one of the best remedies to spend time while at home. We have a 4G network, but there was news that the 5G network would soon become a new normal. Interestingly, even during COVID-19, there were several developments in the 5G network. This article will tell you how the 5G network testing and development stayed intact even during the pandemic.

Innovative Tools That Helped in 5G Testing Even During the Pandemic (Intelligent Site Engineering)#

To continue the 5G testing and deployment even during a pandemic, Telcos used specific innovative tools, the prominent being ISE (Intelligent Site Engineering).

What is Intelligent Site Engineering?#

Intelligent Site Engineering refers to the technique of using laser scanners and drones to design network sites. It is one of the latest ways of network site designing. In this process, they collected every minute detail to create a digital twin of a network site. If the company has a digital twin of a network site, they can operate it anywhere, virtually.

They developed Intelligent Site Engineering to meet the increasing data traffic needs and solve the network deployment problems of the Communication Service Providers (CSPs). This incredible technology enabled the site survey and site design even during the pandemic. We all know that site design and site surveys are vital for the proper installation of a network. But it was not possible to survey the site physically. Therefore, these companies used high technologies to launch and deploy 5G networks even in these lockdown times.

Intelligent Site Engineering uses AI (Artificial Intelligence) and ML (Machine Learning) to quickly and efficiently deliver a network. This helps CSPs to deploy frequency bands, multiple technologies, and combined topologies in one place. This advanced technology marks the transition from the traditional technique of using paper, pen, and measuring tape for a site survey to the latest styles like drones carrying high-resolution cameras and laser scanning devices.

How Does Intelligent Site Engineering Save Time?#

The Intelligent Site Engineering technique saves a lot of time for CSPs. For example, in this digitized version, CSPs take only 90 minutes for a site survey. Previously, they had to waste almost half a day in site surveys using primitive tools in the traditional method. Instead, the engineers can use the time these service providers save for doing other critical work.

Also, this process requires fewer people because of digitalization. This means that it saves the headcount of the workforce and the commuting challenges. Also, it reduces the negative impact on the environment.

What is the Use of Digital Twins Prepared in This Process?#

Using Intelligent Site Engineering, the CSPs replicate the actual site. They copied digital twins through 3D scans and photos clicked from every angle. The engineers then use the copies to get an accurate analysis of the site data. With the highly accurate data, they prepared the new equipment. The best example of digital twins is that CSPs can make a wise decision regarding altering the plan for future networks. Therefore, a digital twin comes in handy, from helping in creating material bills to detailed information about the networking site and related documents.

The technique is helpful for customers as well. For example, through the digital twin, customers can view online documents and sign them. In this way, this advanced technology and innovation enable remote acceptance of network sites even with 5G.

How Did the COVID Pandemic Promote the Digitalization of the 5G Network?#

We all know that the COVID pandemic and the subsequent lockdown put several restrictions on travel. Since no one could commute to the network site, it prepared us to switch to digital methods for satisfying our needs. The result was that we switched to Intelligent Site Engineering for 5G network deployment, bringing in 5G networks innovation.

When physical meetings were restricted, we switched to virtual conversations. Video meetings and conference calls became a new normal during the pandemic. Therefore, communication service providers also used the screen share features to show the clients the network sites captured using drones and laser technology. The image resolution was excellent, and the transition from offline to online mode was successful. Training of the personnel also became digitized.

The best part of this digitalization was that there was no need to have everyone on one site. Using these digital twins and technological tools, anyone can view those designs from anywhere. The companies could share the screen, and the clients could review the site without physical presence.

How Much Efficiency Were the CSPs Able to Achieve?#

When communication service providers were asked about the experience of these new technological tools for network sites, they felt it was better than on-site conversations. They reveal that these online calls help everyone look at the same thing and avoid confusion, which was the biggest problem in on-site meetings. Therefore, this reduces the queries, and teams could complete the deal in less time than offline sales.

The most significant benefit is for the technical product managers. They can now work on online techniques for vertical inspection of assets and sites. In addition, 3D modeling is enhanced, and the ground-level captured images ensure efficiency.

Rounding Up About 5G Networks Innovation:#

The year 2020 was indeed a gloomy year for many of us. But the only silver lining was the announcements of technological advancements like the 5G launch, even during these unprecedented times. The technology advancement enabled us to use this pandemic wisely, and we deployed the 5G network at several places. So, we can say that the innovations remained intact even during the pandemic because of intelligent and relevant technologies. Therefore, it would not be wrong to conclude that technological advancements have won over these challenging times and proved the future.