There is a common debate on how Edge Computing Platforms for Anomalies Detection can be used. In this blog, we will cover details about it.

Introduction#

Anomalies are a widespread problem across many businesses, and the telecommunications sector is no exception. Anomalies in telecommunications can be linked to system effectiveness, unauthorized access, or forgery, and therefore can present in a number of telecommunications procedures. In recent years, artificial intelligence (AI) has become more prominent in overcoming these issues. Telecommunication invoices are among the most complicated invoices that may be created in any sector. With such a large quantity and diversity of goods and services available, mistakes are unavoidable. Products are made up of product specifications, and the massive amount of these features, as well as their numerous pairings, gives rise to such diversity (Tang et al., 2020). Goods and services – and, as a result, the invoicing process – are becoming even more difficult under 5G. Various corporate strategies, such as ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and large machine-type communication, are being addressed by service providers. Alongside 5G, the 3GPP proposed the idea of network slicing (NW slice) and the related service-level agreements (SLAs), adding still another layer to the invoicing procedure's complexities.

How Do Network Operators Discover Invoice Irregularities?#

Invoice mistakes are a well-known issue in the telecom business, contributing to invoicing conflicts and customer turnover. These mistakes have a significant monetary and personal impact on service providers. To discover invoice abnormalities, most network operators use a combination of traditional and computerized techniques. The manual method is typically dependent on sampling procedures that are determined by company regulations, availability of materials, personal qualities, and knowledge. It's sluggish and doesn't cover all of the bills that have been created. These evaluations can now use regulation digitization to identify patterns and provide additional insight into massive data sets, thanks to the implementation of IT in business operations (Preuveneers et al., 2018). The constant character of the telecom business must also be considered, and keeping up would imply a slowdown in the introduction of new goods and services to the marketplace.

How AI and Machine Learning Can Help Overcome Invoice Anomaly Detection#

An AI-based system may detect invoicing abnormalities more precisely and eliminate false-positive results. Non-compliance actions with concealed characteristics that are hard for humans to detect are also easier to identify using AI (Oprea and Bâra, 2021). Using the procedures below, an AI system learns to recognize invoice anomalous behavior from a collection of data:

1. Data from invoices is incorporated into an AI system.
2. Data points are used to create AI models.
3. Every instance a data point detracts from the model, a possible invoicing anomaly is reported.
4. The invoice anomaly is approved by a specific domain.
5. The system applies what it has learned from the activity to the data model for future projections.
6. Patterns continue to be collected throughout the system.

Before delving into the details of AI, it's vital to set certain ground rules for what constitutes an anomaly. Anomalies are classified as follows:

• Point anomalies: A single incident of data is abnormal if it differs significantly from the others, such as an unusually low or very high invoice value.
• Contextual anomalies: A data point that is ordinarily regular but becomes an anomaly when placed in a specific context.
• Collective anomalies: A group of connected data examples that are anomalous when viewed as a whole but not as individual values. When many point anomalies are connected together, they might create collective anomalies (Anton et al., 2018).

Implications of AI and Machine Learning in Anomaly Detection#

All sectors have witnessed a significant focus on AI and Machine Learning technologies in recent years, and there's a reason why AI and Machine Learning rely on data-driven programming to unearth value hidden in data. AI and Machine Learning can now uncover previously undiscovered information and are the key motivation for their use in invoice anomaly detection (Larriva-Novo et al., 2020). They assist network operators in deciphering the unexplained causes of invoice irregularities, provide genuine analysis, increased precision, and a broader range of surveillance.

Challenges of Artificial Intelligence (AI)#

The data input into an AI/ML algorithm is only as strong as the algorithm itself. When implementing the invoice anomaly algorithm, it must react to changing telecommunications data. Actual data may alter its features or suffer massive reforms, requiring the algorithm to adjust to these changes. This necessitates continual and rigorous monitoring of the model. Common challenges include a loss of confidence and data skew. Unawareness breeds distrust, and clarity and interpretability of predicted results are beneficial, especially in the event of billing discrepancies (Imran, Jamil, and Kim, 2021).

Conclusion for Anomaly Detection#

Telecom bills are among the most complicated payments due to the complexity of telecommunications agreements, goods, and billing procedures. As a result, billing inconsistencies and mistakes are widespread. The existing technique of manually verifying invoices or using dynamic regulation software to detect anomalies has limits, such as a limited number of invoices covered or the inability to identify undefined problems. AI and Machine Learning can assist by encompassing all invoice information and discovering different anomalies over time (Podgorelec, Turkanović, and Karakatič, 2019). Besides invoice anomalies, a growing number of service providers are leveraging AI and Machine Learning technology for various applications.

References#

• Anton, S.D., Kanoor, S., Fraunholz, D., & Schotten, H.D. (2018). Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set. Proceedings of the 13th International Conference on Availability, Reliability and Security.
• Imran, J., Jamil, F., & Kim, D. (2021). An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability, 13(18), p.10057.
• Larriva-Novo, X., Vega-Barbas, M., Villagrá, V.A., Rivera, D., Álvarez-Campana, M., & Berrocal, J. (2020). Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets. Applied Sciences, 10(10), p.3430.
• Oprea, S.-V., & Bâra, A. (2021). Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering, 94, p.107329.
• Podgorelec, B., Turkanović, M., & Karakatič, S. (2019). A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection. Sensors, 20(1), p.147.
• Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Applied Sciences, 8(12), p.2663.
• Tang, P., Qiu, W., Huang, Z., Chen, S., Yan, M., Lian, H., & Li, Z. (2020). Anomaly detection in electronic invoice systems based on machine learning. Information Sciences, 535, pp.172–186.

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#

5G has been substantially implemented, and network operators now have a huge opportunity to monetize new products and services for companies and customers. Network slicing is a critical tool for achieving customer service and assured reliability. Ericsson has created the most comprehensive network slicing platform, comprising 5G Radio Access Networks (RAN) slicing, enabling automatic and quick deployment of services of new and creative 5G use scenarios, using an edge strategy (Subedi et al., 2021). Ericsson 5G Radio Access Networks (RAN) Slicing has indeed been released, and telecom companies are enthusiastic about the possibilities of new 5G services. For mobile network operators, using system control to coordinate bespoke network slices in the personal and commercial market sectors can provide considerable income prospects. Ericsson provides unique procedures to ensure that speed and priority are maintained throughout the network slicing process. Not only do they have operational and business support systems (OSS/BSS), central, wireless, and transit systems in their portfolio, but they also have complete services like Network Support and Service Continuity (Debbabi, Jmal and Chaari Fourati, 2021).

What is 5G Radio Access Networks (RAN) Slicing?#

The concept of network slicing is incomplete without the cooperation of communication service providers. It assures that the 5G Radio Access Networks (RAN) Slicing-enabled services are both dependable and effective. Carriers can't ensure slice efficiency or meet service contracts unless they have network support and service continuity. Furthermore, if carriers fail to secure slice performance or meet the service-level agreement, they may face punishment and the dangers of losing clients (Mathew, 2020). Ericsson 5G Radio Access Networks (RAN) Slicing provides service operators with the unique and assured quality they have to make the most of their 5G resources. The novel approach was created to improve end-to-end network slicing capabilities for radio access network managing resources and coordination. As a consequence, it constantly optimizes radio resource allocation and priority throughout multiple slices to ensure service-level commitments are met. This software solution, which is based on Ericsson radio experience and has a flexible and adaptable design, will help service providers to satisfy expanding needs in sectors such as improved broadband access, network services, mission-critical connectivity, and crucial Internet of Things (IoT) (Li et al., 2017).

Ericsson Network Support#

Across complex ecosystems, such as cloud networks, Network Support enables data-driven fault isolation, which is also necessary to efficiently manage the complexity in [5G systems]. To properly manage the complexity of 5G networks, Ericsson Network Support offers data-driven fault isolation. This guarantees that system faults are quickly resolved and that networks are reliable and robust. Software, equipment, and replacement parts are divided into three categories in Network Support. By properly localizing defects and reducing catastrophic occurrences at the solution level, Ericsson can offer quick timeframes and fewer site visits. Ericsson also supports network slicing by handling multi-vendor ecosystem fault separation and resolving complications among domains (Zhang, 2019). Data-driven fault isolation from Ericsson guarantees the quick resolution of connection problems, as well as strong and effective networks, and includes the following innovative capabilities:

• Ericsson Network Support (Software) provides the carrier's software platform requirements across classic, automated, and cloud-based services in extremely sophisticated network settings. It prevents many mishaps by combining powerful data-driven support approaches with strong domain and networking experience.

• Ericsson Hardware Services provides network hardware support. Connected adds advanced technologies to remote activities, allowing for quicker problem identification and treatment. It integrates network data with past patterns to provide service personnel and network management with relevant real-time information. It is feasible to pinpoint errors with greater precision using remote scans and debugging.

• The Spare Components Management solution gives the operator's field engineers access to the parts they need to keep the network up and running (Subedi et al., 2021). Ericsson will use its broad network of logistical hubs and local parts depots to organize, warehouse, and transport the components.

Ericsson Service Continuity#

To accomplish 5G operational readiness, Service Continuity provides AI-powered, proactive assistance, backed by tight cooperation and Always-On service. Advanced analytical automation and reactive anticipatory insights provided by Ericsson Network Intelligence allow Service Continuity services. It focuses on crucial functionality to help customers reach specified business objectives while streamlining processes and ensuring service continuity (Katsalis et al., 2017). It is based on data-driven analysis and worldwide knowledge that is given directly and consists of two services:

• Ericsson Service Continuity for 5G: Enables the clients' networks to take remedial steps forward in time to prevent end-user disruption, allowing them to move from responsive to proactively network services.

• Ericsson Service Continuity for Private Networks is a smart KPI-based support product for Industry 4.0 systems and services that is targeted to the unique use of Private Networks where excellent performance is critical (Mathew, 2020).

Conclusion for 5G Network Slicing

Network slicing will be one of the most important innovations in the 5G network area, transforming the telecommunications sector. The 5G future necessitates a network that can accommodate a diverse variety of equipment and end customers. Communication service providers must act quickly as the massive network-slicing economic potential emerges (Da Silva et al., 2016). However, deciding where to begin or where to engage is difficult. Ericsson's comprehensive portfolio and end-to-end strategy include Network Support and Service Continuity services. Communication service providers across the world would then "walk the talk" for Network Slicing in the 5G age after incorporating them into their network operations plan.

References#

• Da Silva, I.L., Mildh, G., Saily, M. and Hailu, S. (2016). A novel state model for 5G Radio Access Networks. 2016 IEEE International Conference on Communications Workshops (ICC).
• Debbabi, F., Jmal, R. and Chaari Fourati, L. (2021). 5G network slicing: Fundamental concepts, architectures, algorithmics, project practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20).
• Katsalis, K., Nikaein, N., Schiller, E., Ksentini, A. and Braun, T. (2017). Network Slices toward 5G Communications: Slicing the LTE Network. IEEE Communications Magazine, 55(8), pp.146–154.
• Li, X., Samaka, M., Chan, H.A., Bhamare, D., Gupta, L., Guo, C. and Jain, R. (2017). Network Slicing for 5G: Challenges and Opportunities. IEEE Internet Computing, 21(5), pp.20–27.
• Mathew, A., 2020, March. Network slicing in 5G and the security concerns. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 75-78). IEEE.
• Subedi, P., Alsadoon, A., Prasad, P.W.C., Rehman, S., Giweli, N., Imran, M. and Arif, S. (2021). Network slicing: a next-generation 5G perspective. EURASIP Journal on Wireless Communications and Networking, 2021(1).
• Zhang, S. (2019). An Overview of Network Slicing for 5G. IEEE Wireless Communications, [online] 26(3), pp.111–117.

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.

Learn more about Location Based Network Simulators and their need in 5G deployments.

Introduction#

A simulation is a key tool for modeling complicated systems, particularly where traditional analysis methods are unavailable owing to the sophistication of the modeling assumptions. Decision-makers may use simulations to create numerous circumstances and assess the impact of multiple pre-options on the performance of the system (Campanile et al., 2020). Network performance might degrade throughout a traffic increase. Consider viewing a live sports event at a packed arena, where internet traffic is likely to spike. The addition of base stations is frequently used to deal with this influx. Simulators that enable multi-user or multi-cell service have advanced to the point where they may now be often used to simulate some characteristics of actual networks (Graser, Straub, and Dragaschnig, 2013). This blog article will demonstrate how well a simulation may help to optimize the efficiency of your networks and find the best placement for a new base station.

What is Network Optimization and How Does It Work?#

Network optimization is a combination of techniques and practices aimed at improving a network's general health. It manages all components, from the client to the servers, as well as their operations and interconnections. Network optimization allows the user to monitor certain system performance such as data consumption, reliability, network congestion, lag, and oscillation. It provides them with critical information so that they may make the appropriate adjustments (Izquierdo-Zaragoza and Pavon-Marino, 2013). The objective of system optimization is not to purchase additional expensive firmware assets, but to make the most of what customers currently have. Two typical optimisation strategies that can assist enhance the efficiency of these measurements in data and voice systems are Traffic Engineering (TE) and Quality of Service (QoS).

An overview of the data used in the Simulation process.#

Performance Management (PM) Data is used along with numerous KPI (Key Performance Indicator) findings are also included. A KPI is a Meta end-user impression of networks. KPI data are commonly used to compare network performance and identify issue areas (Saino, Cocora, and Pavlou, 2013). PM data is used to extract the GPS coordinates of base stations/cells, which then can then be used to build a base station in a simulator.

Stimulator automation steps for Location Based Simulators#

Simulating a genuine networking ecosystem necessitates several code updates and interactions between people (Rampfl, 2013). The steps of the entire simulation are as follows:-

1. Choose a place for the simulation map.
2. Make a CSV file containing the locations of the base stations.
3. Make a CSV file containing user locations.
4. Create a YAML file with scenario parameters
5. To begin the simulation, run the command line script.
6. The Overpass API is used.
7. The map file has been translated to a simulator-readable format.
8. Users and base stations are depicted as points on a map.
9. Runs and results of simulation

Choosing a stimulation map#

An actual map might be made using the Overpass Turbo website. Overpass Turbo is an internet OSM data-gathering application that makes use of the Overpass API. Choose the map or the actual border with the Overpass API and search for all the things that should be inside the physical boundary (Wolski and Narciso, 2017).

A base station and user location deployment#

The GPS coordinates of the base stations are extracted by using PM data and converted as a CSV file for base station placement (Coskun and Ayanoglu, 2014). The base stations are positioned centrally on the actual location, built by Overpass Turbo web-based service based on the GPS coordinates, using this CSV file as an entry to the simulation.

Creating high-traffic areas and hotspots for users#

A map, assigned customers, and a base station are created, and now it is time for creating the data traffic. Simulators want to dramatically boost traffic in select locations compared to other regions for the practical example. This was accomplished by establishing borders inside a venue and boosting traffic inside those borders while maintaining normal traffic volumes in the nearby region.

YAML file parameters#

The map generation described previously, as well as user and base station installation and traffic parameters, may all be submitted to the simulators through a YAML file. The base station GPS coordinates as a CSV file for base station placement is supplied. Maps may be produced just on run or from a YAML file given by the customer (Garaizar and Reips, 2013). The perimeter criteria and traffic volume parameters were used to manage traffic and define whether it should raise or reduce. Simulators only need to execute it once they have given values for all of the parameters (Campanile et al., 2020). It establishes the external conditions, installs clients and base stations, allocates data traffic, estimates user throughput, and saves the findings in the route supplied depending on the available settings.

Introduction#

In the field of healthcare technology, we are at the start of a high-tech era. AI technology, cloud-based services, the Internet of Things, and big data have all become popular topics of conversation among healthcare professionals as a way to provide high-quality services to patients while cutting costs. Due to ambitions for global application, the fifth generation of cellular technology, or 5G, has gotten a lot of interest. While the majority of media attention has centered on the promise of "the internet of things," the ramifications of 5G-enabled technologies in health care are yet to be addressed (Zhang and Pickwell-Macpherson, 2019). The adoption of 5G in healthcare is one of the elements that is expected to have a significant impact on patient value. 5G, or fifth-generation wireless communications, would not only provide much more capacity but also be extremely responsive owing to its low latency. 5G opens up a slew of possibilities for healthcare, including remote diagnostics, surgery, real-time surveillance, and extended telemedicine (Thayananthan, 2019). This article examines the influence of 5G technology on healthcare delivery and quality, as well as possible areas of concern with this latest tech.

What is 5G?#

The fifth generation of wireless communication technology is known as 5G. Like the preceding fourth generation, the core focus of 5G is speed. Every successive generation of wireless networks improves on the previous one in terms of speed and capability. 5G networks can deliver data at speeds of up to 10 terabytes per second. Similarly, while older networks generally have a delay of 50 milliseconds, 5G networks have a latency of 1–3 milliseconds. With super-fast connection, ultra-low latency, and extensive coverage, 5G marks yet another step ahead (Carlson, 2020). From 2021 to 2026, the worldwide 5G technology market is predicted to grow at a CAGR of 122.3 percent, reaching $667.90 billion. These distinguishing characteristics of 5G enable the possible change in health care as outlined below.

5G's Importance in Healthcare#

Patient value has been steadily declining, resulting in rising healthcare spending. In addition, there is rising concern over medical resource imbalances, ineffective healthcare management, and uncomfortable medical encounters. To address these issues, technologies such as the Internet of Things (IoT), cloud technology, advanced analytics, and artificial intelligence are being developed to enhance customer care and healthcare efficiency while lowering total healthcare costs (Li, 2019). The healthcare business is likely to see the largest improvements as a result of 5G's large bandwidth, reduced latency, and low-power-low-cost. Healthcare professionals investigated and developed several connected-care use cases, but widespread adoption was hampered by the limits of available telecommunications. High-speed and dependable connections will be critical as healthcare systems migrate to a cloud-native design. High data transfer rates, super-low latency, connection and capacity, bandwidth efficiency, and durability per unit area are some of the distinctive properties of 5G technology that have the potential to assist tackle these difficulties (Soldani et al., 2017). Healthcare stakeholders may reorganize, transition to comprehensive data-driven individualized care, improve medical resource use, provide care delivery convenience, and boost patient value thanks to 5G.

5 ways that 5G will change healthcare#

• Large image files must be sent quickly.
• Expanding the use of telemedicine.
• Improving augmented reality, virtual reality, and spatial computing.
• Remote monitoring that is reliable and real-time.
• Artificial Intelligence

Healthcare systems may enhance the quality of treatment and patient satisfaction, reduce the cost of care, and more by connecting all of these technologies over 5G networks (Att.com, 2017). 5G networks can enable providers to deliver more tailored and preventative treatment, rather than just responding to patients' illnesses, which is why many healthcare workers joined providers during the first round.

Challenges#

As with other advances, many industry professionals are cautious about 5G technology's worldwide acceptance in healthcare, as evidenced by the following significant challenges:

• Concerns about privacy and security - The network providers must adhere to the health - care industry's stringent privacy regulations and maintain end-to-end data protection across mobile, IoT, and connected devices.
• Compatibility of Devices - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. As a result, manufacturers have begun to release 5G-enabled smartphones and other products.
• Coverage and Deployment - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. The present 4G network uses certain frequencies on the radio frequency band, often around 6 GHz; however, such systems are available exclusively in a few nations' metro/urban regions, and telecom carriers must create considerable equipment to overcome this difficulty (Chen et al., 2017).
• Infrastructure - As part of the 5G network needs, healthcare facilities, clinics, and other healthcare providers/organizations will need to upgrade and refresh their infrastructure, apps, technologies, and equipment.

Conclusion#

5G has the potential to revolutionize healthcare as we know it. As we saw during the last epidemic, the healthcare business needs tools that can serve people from all socioeconomic backgrounds. Future improvements and gadgets based on new 5G devices and computers can stimulate healthcare transformation, expand consumer access to high-quality treatment, and help close global healthcare inequities (Thuemmler et al., 2017). For enhanced healthcare results, 5G offers network stability, speed, and scalability for telemedicine, as well as catalyzing broad adoption of cutting-edge technologies like artificial intelligence, data science, augmented reality, and the IoT. Healthcare organizations must develop, test, and deploy apps that make use of 5G's key capabilities, such as ultra-high bandwidth, ultra-reliability, ultra-low latency, and huge machine connections.

References#

• Att.com. (2017). 5 Ways 5G will Transform Healthcare | AT&T Business. [online] Available at: https://www.business.att.com/learn/updates/how-5g-will-transform-the-healthcare-industry.html.
• Carlson, E.K. (2020). What Will 5G Bring? Engineering.
• Chen, M., Yang, J., Hao, Y., Mao, S. and Hwang, K. (2017). A 5G Cognitive System for Healthcare. Big Data and Cognitive Computing, 1(1), p.2.
• Li, D. (2019). 5G and Intelligence Medicine—How the Next Generation of Wireless Technology Will Reconstruct Healthcare? Precision Clinical Medicine, 2(4).
• Soldani, D., Fadini, F., Rasanen, H., Duran, J., Niemela, T., Chandramouli, D., Hoglund, T., Doppler, K., Himanen, T., Laiho, J. and Nanavaty, N. (2017). 5G Mobile Systems for Healthcare. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).
• Thayananthan, V. (2019). Healthcare Management using ICT and IoT-based 5G. International Journal of Advanced Computer Science and Applications, 10(4).
• Thuemmler, C., Gavras, A. and Roa, L.M. (2017). Impact of 5G on Healthcare. 5G Mobile and Wireless Communications Technology, pp. 593-613.
• Zhang, M. and Pickwell-Macpherson, E. (2019). The future of 5G Technologies in healthcare. 5G Radio Technologies Seminar.

Introduction#

Consumers want quicker, better, more convenient, and revolutionary data speeds in this internet age. Many people are eager to watch movies on their smartphones while also downloading music and controlling many IoT devices. They anticipate a 5G connection, which will provide 100 times quicker speeds, 10 times more capacity, and 10 times reduced latency. The transition to 5G necessitates significant expenditures from service providers. To support new income streams and enable better, more productive, and cost-effective processes and exchanges, BSS must advance in tandem with 5G network installations (Pablo Collufio, 2019). Let's get ready to face the challenges of 5G monetization.

Why 5G monetization?#

The appropriate 5G monetization solutions may be a superpower, allowing CSPs to execute on 5G's potential from the start. The commercialization of 5G is a hot topic. "Harnessing the 5G consumer potential" and "5G and the Enterprise Opportunity" are two studies that go through the various market prospects. They illustrate that, in the long term, there is a tremendous new income opportunity for providers at various implementation rates, accessible marketplaces, and industry specializations. “Getting creative with 5G business models” highlights how AR/VR gameplay, FWA (Fixed Wireless Access), and 3D video encounters could be offered through B2C, B2B, and B2B2X engagement models in a variety of use scenarios. To meet the 5G commitments of increased network speeds and spectrum, lower latency, assured service quality, connectivity, and adaptable offers, service suppliers must discuss their BSS evolution alongside their 5G installations, or risk being unable to monetize those new use cases when they become a real thing (Munoz et al., 2020). One of the abilities that will enable providers to execute on their 5G promises from day one is 5G monetization. CSPs must update their business support systems (BSS) in tandem with their 5G deployment to meet 5G use scenarios and provide the full promise of 5G, or risk slipping behind in the 5G race for lucrative 5G services (Rao and Prasad, 2018).

Development of the BSS architecture#

To fully realize the benefits of 5G monetization, service providers must consider the growth of their telecom BSS from a variety of angles:

• Integrations with the network - The new 5G Basic standards specify a 5G Convergent Charging System (CCS) with a 5G Charging Function (CHF) that enables merged charging and consumption limit restrictions in the new service-based architecture that 5G Core introduces.
• Service orchestration - The emergence of distributed systems and more business services need more complicated and stricter service coordination and fulfillment to ensure that goods, packages, ordeals, including own and third-party products, are negotiated, purchased, and activated as soon as clients require them.
• Expose - Other BSS apps, surrounding levels such as OSS and Central networks, or 3rd parties and collaborators who extend 5G services with their abilities might all be consumers of BSS APIs (Mor Israel, 2021).
• Cloud architecture - The speed, reliability, flexibility, and robustness required by 5G networks and services necessitate a new software architecture that takes into consideration BSS deployments in the cloud, whether private, public, or mixed.

Challenges to 5G Monetization#

Even though monetizing 5G networks appears to be a profitable prospect for telecommunications, it is not without flaws. The following are the major challenges:

• Massive upfront investments in IT infrastructure, network load, and a radio access system, among other things.
• To get optimal ROI, telecommunications companies must establish viable monetization alternatives (Bega et al., 2019).
• The commercialization of 5G necessitates a change in telecom operations.

Case of Augmented Reality Games and Intelligent Operations#

With the 5G Core, BSS, and OSS in place, it's time to bring on a new partner: a cloud gaming firm that wants to deliver augmented reality monetization strategies to the operator's users (Feng et al., 2020). For gaming traffic, they want a specific network slice with assured service quality. Through a digital platform, a member in a smart, completely automated network may request their network slice and specify their SLAs. BSS decomposes this order into multiple sub-orders, such as the construction and provisioning of the particular portion through the OSS, once it receives it. The operator additionally uses their catalog-driven design to describe the item offered that its customers will acquire to get onboard new on the partner's network slice all in one location. This deal is immediately disseminated to all relevant systems, including online charging, CRM, and digital platforms, and may be generally consumed.

Conclusion#

5G can impact practically every industry and society. Even though there is a lot of ambiguity around 5G and a lot of technical concerns that need to be resolved, one thing is certain: 5G is the next big thing. Finally, whenever a user buys a new plan, he or she is automatically onboarded in the particular portion, often without affecting the system. The partnership will be able to monitor the network health and quality of various types of services for each customer in real time and will be able to take immediate decisions or conduct promotions based on this data (Bangerter et al., 2014). New platforms may adapt to changes based on factual resource use thanks to the BSS cloud architecture. All information regarding purchases, items, network usage, and profitability, among other things, is given back into circulation and utilized as feeds for infrastructure and catalog design in a closed-loop method.

References#

• Bangerter, B., Talwar, S., Arefi, R., and Stewart, K. (2014). Networks and devices for the 5G era. IEEE Communications Magazine, 52(2), pp.90–96.
• Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V. and Costa-Perez, X. (2019). A Machine Learning approach to 5G Infrastructure Market optimization. IEEE Transactions on Mobile Computing, pp.1–1.
• Feng, S., Niyato, D., Lu, X., Wang, P. and Kim, D.I. (2020). Dynamic Game and Pricing for Data Sponsored 5G Systems With Memory Effect. IEEE Journal on Selected Areas in Communications, 38(4), pp.750–765.
• Mor Israel (2021). How BSS can enable and empower 5G monetization. online Available at: https://www.ericsson.com/en/blog/2021/4/how-bss-can-enable-and-empower-5g-monetization.
• Munoz, P., Adamuz-Hinojosa, O., Navarro-Ortiz, J., Sallent, O. and Perez-Romero, J. (2020). Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond. IEEE Access, 8, pp.79604–79618.
• Pablo Collufio, D. (2019). 5G: Where is the Money? e-archivo.uc3m.es. online.
• Rao, S.K. and Prasad, R. (2018). Telecom Operators’ Business Model Innovation in a 5G World. Journal of Multi Business Model Innovation and Technology, 4(3), pp.149–178.

Learn more about Edge Computing and its usage in different fields. Keep reading our blogs.

Edge VMs And Edge Containers are nothing but VMs and Containers used in Edge Locations, or are they different? This topic gives a brief insight into it.

Introduction

If you have just recently begun learning about virtualization techniques, you could be wondering what the distinctions between containers and VMs. The issue over virtual machines vs. containers is at the centre of a discussion over conventional IT architecture vs. modern DevOps approaches. Containers have emerged as a formidable presence in cloud-based programming, thus it's critical to know what they are and isn't. While containers and virtual machines have their own set of features, they are comparable in that they both increase IT productivity, application portability, and DevOps and the software design cycle (Zhang et al., 2018). The majority of businesses have adopted cloud computing, and it has shown to be a success, with significantly faster workload launches, simpler scalability and flexibility, and fewer hours invested on underlying traditional data centre equipment. Traditional cloud technology, on the other hand, isn't ideal in every case.

Microsoft Azure, Amazon AWS, and Google Cloud Platform (GCP) are all traditional cloud providers with data centres all around the world. Whereas each company's data centre count is continually growing, these data centres are not near enough to consumers when an app requires optimal speed and low lag (Li and Kanso, 2015). Edge computing is useful when speed is important or produced data has to be kept near to the consumers.

What is the benefit of Edge Computing?#

Edge computing is a collection of localized mini data centres that relieve the cloud of some of its responsibilities, acting as a form of "regional office" for local computing chores rather than transmitting them to a central data centre thousands of miles away. It's not meant to be a replacement for cloud services, but rather a supplement. Instead of sending sensitive data to a central data centre, edge computing enables you to analyse it at its origin (Khan et al., 2019). Minimal sensitive data is sent across devices and the cloud, which means greater security for both you and your users. Most IoT initiatives may also be completed at a lower cost by decreasing data transit and storage space using traditional techniques.

What are Edge VMs?#

Edge virtual machines (Edge VMs) are technological advancements of standard VM in which the storage and computation capabilities that support the VM are physically closer to the end-users. Each VM is a self-contained entity with its OS, capable of handling almost any program burden (Millhouse, 2018). The flexibility, adaptability, and optimum availability of such tasks are significantly improved by VM designs. Patching, upgrades, and care of the virtual machine's operating system are required regularly. Monitoring is essential for ensuring the virtual machine instances' and underpinning physical hardware infrastructure's stability. Backup and data recovery activities must also be considered. All of this adds up to a lot of time spent on repair and supervision.

What are Edge Containers?#

Edge containers are decentralized computing capabilities that are placed as near to the end customer as feasible in an attempt to decrease delay, conserve data, and improve the overall user experiences. A container is a sandboxed, isolated version of a component of a programme. Containers still enable flexibility and adaptability, although usually isn't for every container in an application framework, only for the one that needs expanding (Pahl and Lee, 2015). It's simple to reboot multiple copies of a container image and bandwidth allocation between them once you've constructed one.

What's the difference Between VMs and Containers even without the context Edge?#

Containers are perfect where your programme supports a microservices design, which allows application programs to function and scale freely. Containers may operate anywhere as long as your public cloud or edge computing platform has a Docker engine (Sharma et al., 2016). Also, there is a reduction in operational and administrative costs. But when your application requires particular operating system integration that is not accessible in a container, VM is still suggested when you need access to the entire OS. VMs are required if you want or need additional control over the software architecture, or if you want or need to execute many apps on the same host.

Next Moves#

Edge computing is a viable solution for applications that require high performance and low latency communication. Gaming, broadcasting, and production are all common options. You may deliver streams of data from near to the user or retain data close to the source, which is more convenient than using open cloud data centres (Sonmez, Ozgovde and Ersoy, 2018). You can pick what is suitable for your needs now that you know more about edge computing, including the differences between edge VMs and edge containers.

Learn more about Edge Computing and its usage in different fields - Nife Blogs

Introduction#

The gaming business, which was formerly considered a specialized sector, has grown to become a giant $120 billion dollar industry in the latest years (Scholz, 2019). The gaming business has long attempted to capitalize on new possibilities and inventive methods to offer gaming adventures, as it has always been the leading result of technology. The emergence of cloud gaming services is one of the most exciting advances in cloud computing technology in recent years. To succeed, today's gamers speed up connections. Fast connectivity contributes to improved gameplay. Gamers may livestream a collection of games on their smartphone, TV, platform, PC, or laptop for a monthly cost ranging from $10 to $35 (Beattie, 2020).

Reasons to buy a gaming computer:

• The gameplay experience is second to none.
• Make your gaming platform future-proof.
• They're prepared for VR.
• Modified versions of your favourite games are available to play.
• More control and better aim.

Why is Hardware PC gaming becoming more popular?#

Gamers are stretching computer hardware to its boundaries to get an edge. Consoles like the PlayStation and Xbox are commonplace in the marketplace, but customers purchasing pricey gaming-specific PCs that give a competitive advantage over the other gamers appear to be the next phenomenon. While the pull of consoles remains strong, computer gaming is getting more and more popular. It was no longer only for the die-hards who enjoy spending a weekend deconstructing their computer. A gaming PC is unrivalled when it comes to providing an unrivalled gaming experience. It's incredible to think that gamers could play the newest FPS games at 60fps or greater. Steam is a global online computer gaming platform with 125 million members, compared to 48 million for Xbox Live (Galehantomo P.S, 2015). Gaming computers may start around $500 and soon grow to $1500 or more, which is one of the most significant drawbacks of purchasing gaming PCs.

The majority of games are now downloadable and played directly on cell phones, video game consoles, and personal computers. With over 3 billion gamers on the planet, the possibility and effect might be enormous (Wahab et al., 2021). Cloud gaming might do away with the need for dedicated platforms, allowing players to play virtually any game on practically any platform. Users' profiles, in-game transactions, and social features are all supported by connectivity, but the videogames themselves are played on the gamers' devices. Gaming has already been growing into the cloud in this way for quite some time. Every big gaming and tech firm seems to have introduced a cloud gaming service in the last two years, like Project xCloud by Microsoft, PlayStation Now by Sony, and Stadia by Google.

Cloud Computing's Advantages in the Gaming World:

• Security
• Compatibility
• Cost-effective
• Accessibility
• No piracy
• Dynamic support

What are Cloud Gaming Services, and how do they work?#

Cloud gaming shifts the processing of content from the user's device to the cloud. The game's perspective is broadcast to the person's devices through content delivery networks with local stations near population centres, similar to how different channels distribute the material. Size does matter, just like it does with video. A modest cell phone screen can show a good gaming feed with far fewer bits than a 55" 4K HDTV. In 2018, digital downloads accounted for more than 80% of all video game sales. A bigger stream requires more data, putting additional strain on the user's internet connection. Cloud streaming services must automatically change the bandwidth to offer the lowest amount of bits required for the best service on a specific device to control bandwidth (Cai et al., 2016).

Edge Gaming - The appeal of Edge Computing in Gaming#

Revenue from mobile gaming is growing more sociable, engaging, and dynamic. As games become more collaborative, realistic, and engaging, mobile gaming revenue is predicted to top $95 billion worth by 2022 (Choy et al., 2014). With this growth comes the difficulty of meeting consumers' desire for ultra-fast, low-latency connectivity, which traditional data centres are straining to achieve. Edge computing refers to smaller data centres that provide cloud-based computational services and resources closer to customers or at the network's edge. In smartphone games, even just a fraction of a millisecond of latency would be enough to completely ruin the gameplay. Edge technology and 5G connection assist in meeting low-latency, high-bandwidth needs by bringing high cloud computing power directly to consumers and equipment while also delivering the capacity necessary for high, multi-player gameplay.

Issues with Cloud Gaming#

Cloud technology isn't only the future of gaming it's also the future of hybridized multi-clouds and edge architecture as a contemporary internet infrastructure for businesses. However, this cutting-edge technology faces a few obstacles. Lag, also known as latency, is a delay caused by the time required for a packet of data to move from one place in a network to another. It's the misery of every online gamer's existence. Streaming video sputters, freezes, and fragments due to high latency networks (Soliman et al., 2013). While this might be frustrating when it comes to video material, it can be catastrophic when it comes to cloud gaming services.

Developers are Ready for the Change#

Gaming is sweeping the media landscape. Please have a look around if you are unaware of this information. Although cloud gameplay is still in its infancy, it serves as proof that processing can be done outside of the device. I hope that cloud gaming is treated as the proving point that it is. Because cloud gameplay always has physical issues, we should look to edge gaming to deliver an experience where gamers can participate in a real-time multiplayer setting.

References#

• https://www.investopedia.com/articles/investing/053115/how-video-game-industry-changing.asp
• Beattie, A. (2020). How the Video Game Industry Is Changing. [online] Investopedia. Available at:
• Cai, W., Shea, R., Huang, C.-Y., Chen, K.-T., Liu, J., Leung, V.C.M. and Hsu, C.-H. (2016). The Future of Cloud Gaming . Proceedings of the IEEE, 104(4), pp.687-691.
• Choy, S., Wong, B., Simon, G. and Rosenberg, C. (2014). A hybrid edge-cloud architecture for reducing on-demand gaming latency. Multimedia Systems, 20(5), pp.503-519.
• Galehantomo P.S, G. (2015). Platform Comparison Between Games Console, Mobile Games And PC Games. SISFORMA, 2(1), p.23.
• Soliman, O., Rezgui, A., Soliman, H. and Manea, N. (2013). Mobile Cloud Gaming: Issues and Challenges. Mobile Web Information Systems, pp.121-128.
• Scholz, T.M. (2019). eSports is Business Management in the World of Competitive Gaming. Cham Springer International Publishing.
• Wahab, A., Ahmad, N., Martini, M.G. and Schormans, J. (2021). Subjective Quality Assessment for Cloud Gaming. J, 4(3), pp.404-419.

Nife started off as an edge computing deployment platform but has moved away to multi-cloud- a hybrid cloud setup

Collated below is some news about Nife and the Platform

• Read about the story of Nife, how it all started and the future months https://yourstory.com/herstory/2021/10/woman-entrepreneur-cloud-edge-computing-nife-labs/amp
• Read about the story of edge computing with Nife and what is in store in the next few months. https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp
• Learnings from Pandemic and more - Antler startups talk about https://www.antler.co/blog/the-good-the-bad-and-the-unexpected-what-entrepreneurs
• Nife is a startup showcasing at the Antler Demo Dayhttps://kr-asia.com/these-are-the-14-startups-ready-for-antlers-southeast-asia-demo-day
• Know how Virtualization and AI Acceleration on cloud and we effectively used Edge in robotics https://link.springer.com/chapter/10.1007/978-981-16-2937-2_10
• 5G offers a unique opportunity for advertisers to serve contextual advertisements in ways never seen before. Read this article to know more about this topic to design your advertisement more efficiently https://wiprotechblogs.medium.com/contextual-ads-winning-in-a-5g-world-94dee05b871d

• Developing Pagemark for Edge Computing. The value of edge computing comes from the data and technologies that adapt to evolving needs.
• https://tecknexus.com/5g-network/developing-pagerank-for-edge-computing/
• https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp

• Finalists of Qualcomm Wommen Entrepreneur India Network (QWEIN) 2021 [Nife] Labs was one of the finalists out of the 12 Startups listed https://yourstory.com/2021/08/qualcomm-women-entrepreneur-india-finalists/amp
• The 5G smart Railway Hackathon Won by 'Nife Labs'Featured in Yourstory about Why Businesses Must Take Note of Edge Computing https://www.startupleadership.com/blog/rec1283/
• Quotes on Digital Transformation https://startupnewsxp.blogspot.com/2021/07/when-ecosystems-become-digital-there.html
• 5G Smart Railway Hackathon. This Hackathon program was part of the 5g Railway Lab in Barcelona and it was fully designed and facilitated by the WorldStratup Lab Unit
• Nife at NetApp Demo Day announcement https://yourstory.com/2021/09/netapp-excellerators-demo-day-unrestrained-startups-spirit/amp
• Nife joins the 8th Cohort of NetApp Excellarator - https://yourstory.com/2021/05/netapp-excellerator-cohort-8-8-startups-using-data-cloud/amp

Learn more about different use cases on edge computing- Nife Blogs