Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Sunday, 15 March 2020

How Cellular IoT and AI Can Help to Overcome Extreme Poverty in a Climate-resilient Way

The Democratic Republic ofthe Congo (DRC) is the second largest country in Africa and it has a significant potential for agricultural development as the country has more land (235 million hectares) than Kenya, Malawi, Tanzania, and Zambia, combined, of which only 3.4% is cultivated.

Despite this, around 13 millions of Congolese live in extreme food insecurity, among them 5 millions acutely malnourished children. Current assessments show the trend is increasing.

In the southern provinces formerly known as "Katanga" the needs in maize for human consumption sum up to 700,000 tons per year, while the local production barely amounts to 120,000 tons per year. This means the provinces have to resort to importing food from neighboring countries, which represents a huge burden on the region's economy.

Another aspect of the problem is that 80% of the local production is made by women farmers, and the biggest challenge they face is the lack of daily agronomic monitoring and guidance. There is only a limited amount of agriculture experts in the region and without assistance, the farmersaverage output is at best one ton per hectare. However, field trials have proven that by using smart farming technology they can easily produce up to 6 tons per hectare year over year with the right sustainable approach and support. Artificial intelligence (AI), the Internet of Things (IoT) and big data analytics underpinned by mobile connectivity can even do more. They bring significant potential for capturing carbon, optimizing water, pesticide and fertilizer usage, and reducing soil erosion. Thus, African women can not only provide the solution to the local food gap/insecurity but also become the primary protectors of their environment.

The basic technical concept is not new. Back in 2016 Ooredoo Myanmar launched Site Pyo, a mobile agriculture information service for smallholder farmers. At its core Site Pyo is a weather forecast app that was enhanced with weather-dependent advice for ten crops, from seed selection to harvesting and storage. In addition the app displays the actual market prices for these crops. GSMA as a co-funder of the project celebrates Site Pyo as a big success, but it seems to be limited to Myanmar. Why?

„A lot of customization needs to be done to adapt the application functionality for a particular region“, says Dieu-Donn√© Okalas Ossami, CEO of „e-tumba“, a French Start-up specialized in smart farming solutions for Sub-Sahara Africa. His company partners with iTK, a spin-off from CIRAD, the French Institute for tropical agronomy. The iTK crop-specific predictive models are based on years of agronomic data, but have originally been designed for big farmers. To meet the demands of women in Katanga requires more granular data for both, input and output.

As in case of Site Pyo weather predictions are important, but in addition there are data feeds from sensors on the spot. Weather stations measure constantly temperature and rainfall while sensors in the soil report its saturation with water, nitrogen and potassium.

„A typical real-time advice that our software provides is to delay the harvest for some additional days to maximize the yield“, explains Okalas Ossami. „However, even for two neighboring fields the particular advices are often different.“ 

Also the communication channels need to be taylored. Many women farmers are illiterate. For them the advice must be translated into the local language they speak and transmitted to their phones as a voice message. Those who can read and write will receive the notifications through short message service.

The mobile connectivity that links all elements of the system is realized by the mobile network operators present in the region.


Infographic: The Technical Environment Behind the Project
„Actually NB-IoT would fit to our use case“, says Okalas Ossami, „but it is not available. And there is neither LoRa nor SigFox.“ Hence, the sensors are using data connections of 3G and 4G radio access technology. In case of network outage or missing coverage a local field technician must collect the sensor data manually and transfer it to the data center through alternative channels.

It is the same field technician who installs the sensors. The woman farmers receive a basic training to understand how the system works, but they do not need to care about technical components - except keeping their mobile phones charged.

Here comes another important aspect into the game: How can the women trust this technical environment?

In case of Site Pyo the operator Ooredoo observed a quickly increasing user community measured by the number of app downloads. However, there was no indication to which extend the Myanmar farmers really used the app. The e-tumba solution addresses this gap by partnering with the non-government organization „Anzafrika“.

Anzafrika is present in the villages where the people live. One of its major targets is to overcome the extreme poverty by developing the regional economy. A key factor for this is that the smallholder farmers do not just see the market prices for their crops, but get real access to large, stable and long-term markets where these prices are paid. Anzafrika is brokering contracts between the woman farmers and large multinational corporations committed to the Economics of Mutuality, growing human, social and natural capital. The business model behind this concept was outlined by Bruno Roche and Jay Jakub in their book „Completing Capitalism:Heal Business to Heal the World“. Instead of focusing on greenhouse gas emissions (output) they insist that climate-resilient business models must measure the input needed for manufacturing goods. As an example: For one hot cup of coffee the greenhouse gas emissions are extremely low, but 3.4 liters of water are needed (most for packaging, processing and drinking) and 12 gram of top soil will be eroded. These are (among others) the expenses paid by the planet that are not taken into account by a carbon tax.

Coffee plantations are monocultures with all the known disadvantages resulting form this kind of farming. In the past the Congolese women farmers have grown maize as a monoculture. Now, with advice from Anzafrika and e-tumba they transitioned from an „all-maize“ sustenance crop to a semi-industrial „maize-sorghum“ production. This helps to minimize the top soil erosion and thus, to remunerate the natural capital involved in the process.  

Regarding the human and social capital Anzafrika monitors how the overall situation in the villages  is improving. The focus is on progress in well-beeing, satisfaction and health not just for the women farmers, but for their entire communities.

In 2019 smart farming technology have been tested and deployed with a group of 150 women in the province of Lualaba. Now, in 2020, their number is expected to rise to 500 and after 6 years the stunning target of 100,000 participants shall be met. A look at the download numbers of Site Pyo (206,000 in the course of one year) shows that these numbers are not over-optimistic.

The partnership between Anzafrika, e-tumba and iTK is now considered as a best international practice, as indicated by Patrick Gilabert, UNIDO Representative to the European Union in Brussels. It fully aligns with the development of new comprehensive strategies for Africa that aim at creating a partnership of equals and mutual interest through agriculture, trade and investment partnerships.

UNIDO, as the UN convener for the implementation of the Industrial Decade for Development of Africa” (IDDA 3) is always ready to join forces with innovative partners.

Wednesday, 12 February 2020

AI your Slice to 5G Perfection


Back in November, The Enhanced Mobile Broadband Group in CW (Cambridge Wireless) held an event on 'Is automation essential in 5G?'. There were some thought provoking presentations and discussions but the one that stood out for me was by Dan Warren from Samsung


The slides are embedded below but I want to highlight these points:
  • Some Network Functions will be per slice whereas others will be multi-slice, the split may not be the same for every slice
  • Two slices that have the same 'per slice vs multi-slice' functional split may be different network hardware topologies
  • Enterprise customers will likely want a 'service' contract that has to be manifested as multiple slices of different types. 
  • Physical infrastructure is common to all slices
The last point is very important as people forget that there is a physical infrastructure that will generally be common across all slices.

Again, when you apply Artificial Intelligence (AI) to optimize the network functions, does it do it individually first and then end-to-end and if this is applied across all slices, each of which may have a different functionality, requirement, etc. How would it work in practice?




As Dan says in his tweet, "It is hard to implement AI to optimise a point solution without potentially degrading the things around it.  Constantly being pushed to a bigger picture view => more data => more complexity"

Let me know what you think.

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Saturday, 29 June 2019

Presentations from ETSI Security Week 2019 (#ETSISecurityWeek)


ETSI held their annual Security Week Seminar 17-21 June at their HQ in Sophia Antipolis, France. All the presentations are available here. Here are some I think the audience of this blog will like:


Looks like all presentations were not shared but the ones shared have lots of useful information.


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Wednesday, 6 February 2019

AI in 5G – the why and how

IET recently held the 6th Annual 5G conference bringing together key players in the 5G world. You can watch the videos for that event here (not all have been uploaded at the time of writing this post).

We reached out to Dr. Yue Wang to share her presentation with us and she has kindly done so. The presentation and video are embedded below.






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Wednesday, 23 January 2019

AI and Analytics Based Network Designing & Planning

Recently I blogged about how Deutsche Telekom is using AI for variety of things. The most interesting being (from this blog point of view), fiber-optic roll-out. According to their press release (shortened for easy reading):

"The shortest route to the customer is not always the most economical. By using artificial intelligence in the planning phase we can speed up our fiber-optic roll-out. This enables us to offer our customers broadband lines faster and, above all, more efficiently," says Walter Goldenits, head of Technology at Telekom Deutschland. It is often more economical to lay a few extra feet of cable. That is what the new software-based technology evaluates using digitally-collected environmental data. Where would cobblestones have to be dug up and laid again? Where is there a risk of damaging tree roots?

The effort and thus costs involved in laying cable depend on the existing structure. First, civil engineers open the ground and lay the conduits and fiber-optic cables. Then they have to restore the surface to its previous condition. Of course, the process takes longer with large paving stones than with dirt roads.

"Such huge amounts of data are both a blessing and a curse," says Prof. Dr. Alexander Reiterer, who heads the project at the Fraunhofer IPM. "We need as many details as possible. At the same time, the whole endeavour is only efficient if you can avoid laboriously combing through the data to find the information you need. For the planning process to be efficient the evaluation of these enormous amounts of data must be automated." Fraunhofer IPM has developed software that automatically recognizes, localizes and classifies relevant objects in the measurement data.

The neural network used for this recognizes a total of approximately 30 different categories through deep learning algorithms. This includes trees, street lights, asphalt and cobblestones. Right down to the smallest detail: Do the pavements feature large pavement slabs or small cobblestones? Are the trees deciduous or coniferous? The trees' root structure also has a decisive impact on civil engineering decisions.

Once the data has been collected, a specially-trained artificial intelligence is used to make all vehicles and individuals unidentifiable. The automated preparation phase then follows in a number of stages. The existing infrastructure is assessed to determine the optimal route. A Deutsche Telekom planner then double-checks and approves it.


In the recent TIP Summit 2018, Facebook talked about ‘Building Better Networks with Analytics’ and showed off their analytics platform. Vincent Gonguet, Product Manager, Connectivity Analytics, Facebook talked about how Facebook is using a three-pronged approach of accelerating fiber deployment, expanding 4G coverage and planning 5G networks. The video from the summit as follows:

TIP Summit 2018 Day 1 Presentation - Building Better Networks with Analytics from Telecom Infra Project on Vimeo.

Some of the points highlighted in the video:
  • Educating people to connect requires three main focus areas, Access, Affordability and Awareness – One of the main focus areas of TIP is access. 
  • 4G coverage went from 20% to 80% of world population in the last 5 years. The coverage growth is plateauing because the last 20% is becoming more and more uneconomical to connect.
  • Demand is outpacing supply is many parts of the world (indicating that networks has to be designed for capacity, not just coverage)
  • 19% of 4G traffic can’t support high quality videos today at about 1.5 Mbps
  • Facebook has a nice aggregated map of percentage of Facebook traffic across the world that is experiencing very low speeds, less than 0.5 Mbps
  • Talk looks at three approaches in which Facebook works with TIP members to accelerate fiber deployment, expand 4G coverage and plan 5G networks.
  • A joint fiber deployment project with Airtel and BCS in Uganda was announced at MWC 2018
  • 700 km of fiber deployment was planned to serve over 3 million people (Uganda’s population is roughly 43 million)
  • The real challenge was not just collecting data about roads, infrastructure, etc. New cities would emerge over the period of months with tens of thousands of people 
  • In such situations it would be difficult for human planners to go through all the roads and select the most economical route. Also, different human planners do thing in different ways and hence there is no consistency. In addition, its very hard to iterate. 
  • To make deployments simpler and easier, it was decided to first provide coverage to people who need less km of fiber. The savings from finding optimal path for these people can go in connecting more people.
  • It is also important for the fiber networks to have redundancy but it’s difficult to do this at scale
  • An example and simulation of how fiber networks are created is available in the video  from 07:45 – 11:00.
  • Another example is that of prioritizing 4G deployments based on user experience, current network availability and presence of 4G capable devices in partnership with XL Axiata is available in the video from 11:00 – 14:13. Over 1000 sites were deployed and more than 2 million people experienced significant improvement in their speeds and the quality of videos. 
  • The final example is planning of 5G mmWave networks. This was done in partnership with Deutsche Telekom, trying to bring high speeds to 25,000 apartment homes in a sq. km in the center of Berlin. The goal was to achieve over 1Gbps connection using a mixture of fiber and wireless. The video looks at the simulation of Lidar data where the wireless infrastructure can be deployed. Relevant part is from 14:13 – 20:25.
Finally, you may remember my blog post on Automated 4G / 5G Hetnet Design by Keima. Some of the work they do overlaps with both examples above. I reached out to Iris Barcia to see if they have any comments on the two different approaches above. Below is her response:

“It is very encouraging that DT and Facebook are seeing the benefits of data and automation for design. I think that is the only way we’re going to be able to plan modern communication networks. We approach it from the RAN planning perspective: 8 years ago our clients could already reduce cost by automatically selecting locations with good RF performance and close to fibre nodes, alternatively locations close to existing fibre routes or from particular providers. Now the range of variables that we are capable of computing is vast and it includes aspects such as accessibility rules, available spectrum, regulations, etc. This could be easily extended to account for capability/cost of deploying fibre per type of road. 

But also, we believe in the benefit of a holistic business strategy, and over the years our algorithms have evolved to prioritise cost and consumers more precisely. For example, based on the deployment needs we can identify areas where it would be beneficial to deploy fibre: the study presented at CWTEC showed a 5G Fixed Wireless analysis per address, allowing fibre deployments to be prioritised for those addresses characterised by poor RF connectivity.”

There is no doubt in my mind that more and more of these kinds of tools that relies on Analytics and Artificial Intelligence (AI) will be required to design and plan the networks. By this I don’t just mean 5G and other future networks but also the existing 2G, 3G & 4G networks and Hetnets. We will have to wait and see what’s next.


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Friday, 26 October 2018

The Yin and the Yang of AI & Blockchain


Today I read about HTC's Exodus 1, new Blockchain smartphone that only people with crypto-currency can buy. SCMP described in very simple terms what this phone is for:

Both HTC’s Exodus and Sirin’s Finney smartphones feature a built-in digital wallet application that will enable users to securely store and use cryptocurrencies, such as bitcoin and ethereum, in daily transactions.

Those smartphones are designed to replace the special memory sticks, which employ complex usernames and passwords to access, that cryptocurrency investors use to store their digital money. These investors typically store most of their cryptocurrencies in such hardware, which are kept offline as a means of security.

“There are things that a phone manufacturer can do with a chip that nobody else can,” said Chen. “We want to be safer than the existing hardware wallets … HTC has a track record of making trusted hardware.”

The company’s Exodus smartphone, for example, can serve as a “node”, which can connect to certain blockchain networks to enable trading of tokens between users. It will also be able to act as a so-called mining rig for users to earn new tokens tied to the Exodus blockchain.

“At some point, we’ll do our own utility token,” said Chen, adding that there was no timetable for such a token release.

HTC’s foray into blockchain, the distributed ledger technology behind cryptocurrencies like bitcoin, represents a strategy to keep the company relevant in smartphones, which is a market dominated by Samsung Electronics and Apple, followed by Huawei Technologies, Xiaomi and other major Chinese brands.

Anyway, the blockchain smartphone reminds me of the joke above (via marketoonist). The second technology mentioned in this joke is AI or Artificial Intelligence.

I heard HP Enterprise talk about AI recently and this picture above is a nice simple way to show how Deep Learning (DL), Artificial Neural Networks (ANN), Machine Learning (ML) and Artificial Intelligence (AI) are related.

I see AI and blockchain often referred to together. This does not necessarily mean that they are related. iDate allowed me to share a recent presentation (embedded below) that refers to AI & blockchain as Yin and Yang. Anyway, I am happy to learn more so if you have any thoughts please feel free to share.



Further Reading:


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Tuesday, 2 October 2018

Benefits and Challenges of Applying Device-Level AI to 5G networks


I was part of Cambridge Wireless CWTEC 2018 organising committee where our event 'The inevitable automation of Next Generation Networks' covered variety of topics with AI, 5G, devices, network planning, etc. The presentations are available freely for a limited period here.

One of the thought provoking presentations was by Yue Wang from Samsung R&D. The presentation is embedded below and can be downloaded from Slideshare.



This presentation also brought out some interesting thoughts and discussions:

  • While the device-level AI and network-level AI would generally work cooperatively, there is a risk that some vendor may play the system to make their devices perform better than the competitors. Something similar to the signaling storm generated by SCRI (see here).
  • If the device-level and network-level AI works constructively, an operator may be able to claim that their network can provide a better battery life for a device. For example iPhone XYZ has 25% better battery life on our network rather than competitors network.
  • If the device-level and network-level AI works destructively for any reason then the network can become unstable and the other users may experience issues. 

I guess all these enhancements will start slowly and there will be lots of learning in the first few years before we have a stable, mutually beneficial solution.

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Monday, 13 August 2018

Telefonica: Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to Connect the Unconnected


Earlier, I wrote a detailed post on how Telefonica was on a mission to connect 100 Million Unconnected with their 'Internet para todos' initiative. This video below is a good advert of what Telefinica is trying to achieve in Latin America


I recently came across a LinkedIn post on how Telef√≥nica uses AI / ML to connect the unconnected by Patrick Lopez, VP Networks Innovation @ Telefonica. It was no brainer that this needs to be shared.



In his post, Patrick mentions the following:

To deliver internet in these environments in a sustainable manner, it is necessary to increase efficiency through systematic cost reduction, investment optimization and targeted deployments.

Systematic optimization necessitates continuous measurement of the financial, operational, technological and organizational data sets.

1. Finding the unconnected


The first challenge the team had to tackle was to understand how many unconnected there are and where. The data set was scarce and incomplete, census was old and population had much mobility. In this case, the team used high definition satellite imagery at the scale of the country and used neural network models, coupled with census data as training. Implementing visual machine learning algorithms, the model literally counted each house and each settlement at the scale of the country. The model was then enriched with crossed reference coverage data from regulatory source, as well as Telefonica proprietary data set consisting of geolocalized data sessions and deployment maps. The result is a model with a visual representation, providing a map of the population dispersion, with superimposed coverage polygons, allowing to count and localize the unconnected populations with good accuracy (95% of the population with less than 3% false positive and less than 240 meters deviation in the location of antennas).


2. Optimizing transport



Transport networks are the most expensive part of deploying connectivity to remote areas. Optimizing transport route has a huge impact on the sustainability of a network. This is why the team selected this task as the next challenge to tackle.

The team started with adding road and infrastructure data to the model form public sources, and used graph generation to cluster population settlements. Graph analysis (shortest path, Steiner tree) yielded population density-optimized transport routes.


3. AI to optimize network operations


To connect very remote zones, optimizing operations and minimizing maintenance and upgrade is key to a sustainable operational model. This line of work is probably the most ambitious for the team. When it can take 3 hours by plane and 4 days by boat to reach some locations, being able to make sure you can detect, or better, predict if / when you need to perform maintenance on your infrastructure. Equally important is how your devise your routes so that you are as efficient as possible. In this case, the team built a neural network trained with historical failure analysis and fed with network metrics to provide a model capable of supervising the network health in an automated manner, with prediction of possible failure and optimized maintenance route.

I think that the type of data driven approach to complex problem solving demonstrated in this project is the key to network operators' sustainability in the future. It is not only a rural problem, it is necessary to increase efficiency and optimize deployment and operations to keep decreasing the costs.


Finally, its worth mentioning again that I am helping CW (Cambridge Wireless) organise their annual CW TEC conference on the topic 'The inevitable automation of Next Generation Networks'. There are some good speakers and we will have similar topics covered from different angles, using some other interesting approaches. The fees are very reasonable so please join if you can.

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Tuesday, 13 February 2018

Artificial Intelligence - Beyond SON for Autonomous Networks


What is the next step in evolution of SON? Artificial Intelligence obviously. The use of artificial intelligence (AI) techniques in the network supervisory system could help solve some of the problems of future network deployment and operation. ETSI has therefore set up a new 'Industry Specification Group' on 'Experiential Networked Intelligence' (ISG ENI) to develop standards for a Network Supervisory assistant system.


The ISG ENI focuses on improving the operator experience, adding closed-loop artificial intelligence mechanisms based on context-aware, metadata-driven policies to more quickly recognize and incorporate new and changed knowledge, and hence, make actionable decisions. ENI will specify a set of use cases, and the generic technology independent architecture, for a network supervisory assistant system based on the ‘observe-orient-decide-act’ control loop model. This model can assist decision-making systems, such as network control and management systems, to adjust services and resources offered based on changes in user needs, environmental conditions and business goals.


The introduction of technologies such as Software-Defined Networking (SDN), Network Functions Virtualisation (NFV) and network slicing means that networks are becoming more flexible and powerful. These technologies transfer much of the complexity in a network from hardware to software, from the network itself to its management and operation. ENI will make the deployment of SDN and NFV more intelligent and efficient and will assist the management and orchestration of the network.


We expect to complete the first phase of ENI work in 2019. It will include a description of use cases and requirements and terminology, including a definition of features, capabilities and policies, which we will publish in a series of informative best practice documents (Group Reports (GRs)).
This will of course require co-operation from many different industry bodies including GSMA, ITU-T, MEF, IETF, etc.

Will see how this goes.

Further reading: