Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Image for post

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
Image for post

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

What is GEEK

Buddha Community

Autonomous Driving Network (ADN) On Its Way

Olivia Green

1611921621

Network management is concerned with monitoring and managing network devices within an organization, including routers, switches, endpoints, and so on. The purpose of network management is to ensure the correct operation of devices, adequate allocation of resources between applications and services, as well as maintaining the quality and availability of services, impacts your brand reputations. It also includes ensuring the visibility and transparency of the network and protects against unauthorized access and data leakage.

Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Image for post

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
Image for post

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

Vikas  Sharma

Vikas Sharma

1623574551

Vikas Driving School Melbourne - Cheap Indian Driving Lessons

Experts say that you can’t become a skilled driver within a week. Driving, like many other skills, requires patience and lots of practice. Even if you’ve been driving for several years, it’s always a good strategy to learn new techniques and keep improving.

In this article, we’ll explain a few tricks that’ll help you become a better driver and avoid unexpected road accidents. These tricks will include everything, starting from taking professional driving lessons in Melbourne to properly using all the features in your car.

So, without any further ado, let’s get started.

1. Learn from Experts

If you’re an absolute beginner, the first step towards becoming a better driver would be to join a dedicated driving school in Melbourne. These schools have professional driving instructors who have years of experience in training novice drivers.

They’ll help you understand the basics of driving and also give you extra tips to stay confident behind the wheel. Another potential benefit of joining a driving school is that it’ll also help you pass the driving license test more easily. Why? Because the instructors will also share different rules and regulations that you must follow during the test.

2. Always Set Your Mirrors Correctly

Another crucial tip that’ll help you become a better driver is to adjust all the mirrors correctly. Many people keep the side mirrors too close that they only see the rear portion of their car and not the actual road.

Keep in mind that if you’re doing this, you won’t be able to know how many cars are behind you and it’ll become challenging to change lanes. So, learn how to adjust the slider mirror and the internal rearview mirror so that you always have a clear view of the back.

3. Maintain a Safe Distance From Other Cars in Traffic

While driving in traffic, make it a habit to maintain a safe distance from the car in front of you. The general thumb rule says that you should keep a distance of at least two full-length cars from the cars in front of you. This way even if the other driver brakes hard, you won’t go colliding into his/her vehicle.

4. Always Use Indicators While Changing Lanes

When it comes to driving in traffic, it’s quite natural to change lanes from time to time. However, switching lanes without using any signals may confuse other drivers and may become the reason for unexpected accidents.

So, while changing lanes, make sure to check the side and rearview mirrors first and then use the correct indicator. If you’re a beginner, your instructor from the driving school in Melbourne will ask you to master this tactic.

#driving school melbourne #driving school near me #driving school south morang #driving lessons melbourne #driving school werribee

Seamus  Quitzon

Seamus Quitzon

1593304560

Autonomous driving market overview

Indeed, just like we turned from horses to cars about a 100 years ago, mobility is slowly turning from mechanical transportation machines to supercomputers on wheels; creating a new land of opportunities for outsiders to come in and for balances of power to shift drastically in a trillion dollar automotive industry.

“Autonomous driving is at the heart of what is considered the second inflection point of mobility.”

Since autonomous driving activities kicked off with the DARPA challenge in 2004, the ecosystem became a lot larger and fiercely competitive with OEMs and tier 1 suppliers now joined by internet companies, TELCOs, electronics manufacturers, and a large crowd of startups. A spurge of innovation and enthusiasm notably took the market by storm from 2013 to 2017 with expectations that autonomous cars would be widely adopted by 2020.

Billions of funding later, where is autonomous driving standing right now?

The promise of a better future

What’s a self-driving car? In simple words, a self-driving car is a car with the ability to perceive the outside environment and to make driving decisions upon it, and thus, drive by itself. It usually takes the form of a car retrofitted with a bunch of sensors (e.g. cameras, lidars, and radars) and powered by an embedded supercomputer trained to be a super driver (sort of substituting for the eyes and the brain of the driver).

What sounded like science fiction 20 years ago has never seemed so real today with autonomous cars quickly becoming the greatest hope to get rid of bad human drivers (e.g. responsible of 90% of accidents on the road), offer mobility to people with health problems and disabilities, reduce congestion in cities (e.g. traffic jams and parking), increase productivity for soon to be ex-drivers, have robots deliver packages to our doors, or even to bring many mobility companies in profitable territory (e.g. taxis and ride hailing companies).

“Humans spend on average thousands of hours in cars (including hundreds of hours stuck in traffic or looking for parking), while also being held responsible for 90% of road accidents.”

These perspectives for innovation drove tech companies to take the market by storm which in turn, thanks to majorbreakthroughs in artificial intelligence and machine learning, led to unprecedent progress and massive amounts of funding. The boom of the industry in the mid-2010s further drew over-optimistic predictions with a wide range of actors such as General Motors, Ford, Google’s Waymo, Toyota, Honda, and Tesla all promising us autonomous cars around 2020.

Yet, 2020 is here and self-driving cars aren’t.

Overcoming the data challenge

Autonomous driving is at the crossroad of many challenges which are likely to unfold as followed: technology, and then, in a lesser extent, regulation and adoption.

First and foremost, autonomous driving companies need to make sure that the tech is solid and that self-driving cars are as close as possible to being 100% safe, making a case at surpassing their human counterparts. In fact, autonomous cars being 99% safe would result in killing 1 every 100 pedestrians crossed and would amount to millions of deaths very quickly. Making self-driving cars work is extremely difficult and involves a set of very complex technologies such as computer vision, artificial intelligence, or machine learning for cars to learn how to drive from data accumulated on the road and that will be further transformed in hard-coded computer rules.

Once the tech is ready, governments and public institutions will need to lay the ground to introduce and regulate autonomous cars in our day to day transportation. That includes granting authorizations to autonomous driving companies to test and to drive in public areas as well as agreeing on a legislation defining rules and responsibilities (e.g. liability in accidents, proper data collection, sharing of the road for self-driving and regular cars).

Lastly, it will be important to provide user friendly commercial applications and to educate people about self-driving cars in order to enhance public adoption. Today, more than 50% of people still don’t feel comfortable about riding autonomous cars. Moreover, even though younger generations are more inclined to shared mobility services and new technologies, cars have long been meaningful possessions advocating for freedom and social status.

Each of these challenges will take some time to be addressed but what has truly been delaying commercial roll plans is the technical complexity of building the technological stack. Over the last 10 years, the industry has laid the ground to capture, store, and process billions of hours of driving data to teach cars how to behave in increasingly complicated scenarios, but self-driving cars now seem to be confronted to the limitations of big data.

Indeed, in order to learn from new scenarios, self-driving cars need to run into infrequent situations, or “edge cases”, which require an ever-larger number of miles to be driven and data to be accumulated. The trick in improving autonomous driving is that the closer we get to the 100% safety mark, the more infrequent the edge cases are, the more data we need to find them, and the exponentially more difficult progress becomes.

In 2017, Intel notably claimed that self-driving cars generated between 1TO and 5TO of data per hour per test vehicle, which for companies running fleets of tests vehicles all day long represents the equivalent of an ocean of data. Working through these massive amounts of data proved to be highly complex, time consuming, expensive, and unsustainable; forcing many companies to reconsider their go-to-market.

“Self-driving cars generate between 1TO and 5TO of data per hour per vehicle.”

Who’s leading the autonomous driving race?

Despite the bumps in the road, many companies are stepping up their efforts and betting that future gains will far outweigh the burdens of bringing the technology to market.

To help us track the race towards fully automated vehicles, the Society of Automotive Engineers (SAE) notably described autonomous driving in 6 levels of automation ranging from no automation to full automation.

At its core, the tech behind autonomous driving relies on capturing, processing, and deploying data that will power a self-driving software. The driving software is the holy grail of the industry and many tech companies leveraged their unfair advantage in software and data management.

“Not surprisingly, the leader in autonomous driving today isn’t a car manufacturer but Waymo: a Google spinoff.”

Autonomous driving performance has been measured in a few (maybe debatable) metrics that became standards in the industry, one of which being the miles driven per disengagement reported at the Department of Motor Vehicles (DMV) in California (level 3 and above).

In 2019, surprisingly and for the first time since 2015, Waymo came second in miles per disengagement behind Baidu rising to the top with over 18 000 miles per disengagement. However, Waymo and GM Cruise largely dominated other metrics with 1.4 million miles driven by 148 test vehicles and 0.8 million miles driven by 228 test vehicles respectively (e.g. Baidu drove 0.1 million miles with 4 test vehicles).

It’s worth noting that if startups seem to be leading the way in autonomous driving performance, large corporations tend to be more secretive about testing while also filing more patents to protect intellectual property.

In terms of commercialization and state of the art, autonomous driving is still very limited with Tesla proposing the most sophisticated self-driving car on the market with level 2/3 automation and a few actors offering level 4 automation in specific use cases (e.g. parcel and goods delivery, residential transportation).

Hence, besides all the excitement about autonomous driving and all the big announcements about commercial plans rolling out anytime soon, we’re still a long way from level 5 and full automation in our day to day transportation.

“It could take up to a few decades before autonomous cars are widely adopted.”

#self-driving-cars #mobility #autonomous-driving #autonomous-cars #cars #mobile app

Games DApp

Games DApp

1606981211

Matic Network in Blockchain Gaming

Matic Network is getting lots of attraction amidst the blockchain game developers. This is because, their competition has stepped away from the gaming scene. Matic - as a general purpose platform, capable of creating all types of DApps, and have already build 60+ DApps on Matic Network.

As a result Matic Network is busy gaining a lots of new gaming partners. They have already been integrated into many gaming DApps.

Key reasons why DApps chooses Matic Network

  • Near-instant blockchain transactions
  • Low Transaction fees >> less than 1/1000th of the fees on the Ethereum mainchain
  • Seamless migration for existing Ethereum DApps
  • Access to, and assistance with, a wide range of developer tooling.
  • Unparalleled technical support for developers.

If you have an idea to build your own Gaming DApp - you could benefit from matic network’s high-speed, low-fee infrastructure and our assistance to transform your DApp from a great idea into a successful DApp business.

Being a Predominant DApp Game Development Company, GamesDApp helps you to Build DApp Game on matic network and also expertize in developing various popular games on the blockchain network using smart contract.

Hire Blockchain Game Developers >> https://www.gamesd.app/#contactus

#matic network #build dapp game on matic network #dapp game on matic network #matic network in blockchain gaming #matic network for game development

antonio marsh

antonio marsh

1606393360

Avail our world-class XOXO Network Smart Contract and earn huge profits

Automate all your daily functions by indulging in XOXO Network MLM Clone. The advantages include an unstoppable protocol system, presence of the Ethereum smart contract, a peer to peer payments system, no chance of hacking or scamming, availability of seven global auto pools, limitless referral bonus, an initial investment of only 0.1 ETH, and a 100% secure system.

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