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What Drives Edge Computing?

What Drives Edge Computing?

Data is exploding in every respect: in data volume, data velocity, and data variety (the 3 v’s). One driver of this phenomenon is the growing number of Mobile and IoT devices and thus, data sources. Making this data useful is one of the driving forces behind the adoption of Edge Computing. New use cases don’t only rely on using this data, but also upon the usability and speed of usability of this ever growing data. There are several practical challenges with this growing data volume that drive the adoption of Edge Computing:

Growing Data Volumes Drive Edge Computing

what-drives-edge-computing

1) Bandwidth Limitations: The existing network infrastructure cannot support sending all the data to the cloud. Particularly in urban areas there is a concentration of devices and data overburdens existing infrastructure. While 5G promises some relief, it is no hailbringer. First of all, if you want to implement your IoT project now, 5G is not yet available and many questions about 5G remain, e.g. pricing. But moreover, as the number of devices and data is growing ever faster, it is already clear that data volumes will outpace what 5G can support. Edge Computing will be an important technology alongside 5G to enable IoT.

2) Use Case Viability: Response time requirements are growing at the same time as data volumes are increasing. Sending data to the cloud for computation and storage means applications’ response times have a higher latency and depend on the network, which cannot guarantee response rates. Use cases that need speedy response times or guaranteed responses cannot rely on cloud computing. For example, in driver assistance, where every millisecond counts or in factory floors, where downtimes are too costly.

3) Sustainability: Sending all data to the cloud and storing it there is costly; it often outweighs financial benefits of the application, particularly as projects scale. Cloud computing also harm the environment, with data centers predicted to consume as much as 21% of the total global energy by 2030. [1]

To scale your prototype, you need to move to the edge

At the start of IoT projects, quick prototyping, testing and piloting on early iterations of an application’s functionalities, can effectively be done in the cloud. However, cloud based computing does not scale for productive environments and is not viable for many use cases. This drives the need to move to the edge.

At the same time, decreasing hardware costs and hardware sizes are enabling more complex local computing, meaning there is less need for additional cloud usage. E.g. increasingly AI and ML is done at the edge, including model training.

data-volume-edge-computing-solution-iot-mobile

ObjectBox database and synchronization

Today’s successful businesses require a smarter approach to data management and integration. Data synchronization increases operational efficiencies, saving time and resources by eliminating redundant data transfer. With data synchronization, only predefined, useful parts of a data set are sent to a central instance. This means that while large volumes of data can be collected and analyzed locally, not all of this data is sent to and saved in the cloud. This reduces the impact on bandwidth, utilizes the local hardware resources for fast guaranteed response times, and keeps project cloud costs low – ultimately creating a more sustainable and efficient model of data architecture, enabling long term project scalability. 

ObjectBox’ current database technology is enabling companies to persist and use data on edge devices, faster than any alternative on the market. This is a pre-requisite for true Edge Computing. On top, ObjectBox Sync is at a stage that allows developers to synchronize relevant information pieces of data from and to the edge with a central instance (e.g. an on-premise server or the cloud). Building upon this, ObjectBox plans to extend synchronization features to enable distributed synchronization and peer-to-peer synchronization, incorporating conflict resolution strategies. This enables networks of edge devices working without a central instance, enabling even more new use cases.

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The ObjectBox edge database and data synchronization solution solves
data persistence on the edge with speed, reliability, and ease.

10X Faster than
any Alternative

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From Edge
to Cloud

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Sync Data
Seamlessly

How Building Green IoT Solutions on the Edge Can Help Save Energy and CO2

How Building Green IoT Solutions on the Edge Can Help Save Energy and CO2

The internet of things (IoT) has a huge potential to reduce carbon emissions, as it enables new ways of operating, living, and working [1] that are more efficient and sustainable. However, IoT’s huge and growing electricity demands are a challenge. This demand is due primarily to the transmission and storage of data in cloud data centers. [2] While data center efficiency and the use of green energy will reduce the CO2 emissions needed for this practice, it is not addressing the problem directly. [3

iot-data-cloud-energy-waste

With ObjectBox, we address this unseen and fast-growing CO2 source at the root: ObjectBox empowers edge computing, reducing the volume of data transmitted to central data storage, while at the same time, heightening data transmission and storage efficiency. [4] We’ve talked before about how edge computing is necessary for a sustainable future, below we dive into the numbers a bit deeper. TLRD: ObjectBox enables companies to cut the power consumption of their IoT applications, and thus their emissions, by 50 – 90%. For 2025, the potential impact of ObjectBox is a carbon emission reduction of 594 million metric tons (see calculations below).

How ObjectBox’ Technology Reduces Overall Data Transmission

 ObjectBox reduces data transmission in two ways: 1. ObjectBox reduces the need for data transmission, 2. ObjectBox makes data transmission more efficient. ObjectBox’ database solution allows companies to build products that store and process data on edge devices and work with that data offline (as well as online). This

not only improves performance and customer experience, it also reduces the overall volume of data that is being sent to the cloud, and thus the energy needed to transfer the data as well as store it in the cloud. ObjectBox’ Synchronization solution makes it easy for companies to transmit only the data that needs to be transmitted through 1) selective two-way syncing and 2) differential delta syncing. Synchronizing select data reduces the energy required for unnecessarily transmitting all data to the cloud.

We have demonstrated in exemplary case studies that ObjectBox can reduce total data transmissions by 70-90%, depending on the case. There will, however, typically be value in transmitting some parts of data to a central data center (cloud); ObjectBox Sync combines efficient compression based on standard and proprietary edge compression methods to keep this data small. ObjectBox also has very little overhead. Comparing the transmission of the same data sets, ObjectBox saves 40-60% on transmission data volume through the delta syncing and compression, and thus saves equivalent CO2 emissions for data transmissions. Additional studies support these results, and have shown that moving from a centralized to a distributed data structure, saves between 32 and 93% of transmission data. [5

sync-sustainable-data-save-energy

Calculations: How Does ObjectBox Save CO2?

Physically using a device consumes little energy directly; it is the wireless cloud infrastructure in the backend (data center storage and data transmission) that is responsible for the high carbon footprint of mobile phones [6] and IoT devices. Estimates say that IoT devices will produce around 2,8 ZB of data in 2020 (or 2,823,000,000,000  GB), globally. [7] Only a small portion of that data actually gets stored and used; we chose to use a conservative estimate of 5% [8] (141,150,000,000 GB) and of that portion, 90% is transferred to the cloud [9] (127,035,000,000 GB). Transferring 1 GB of data to the cloud and storing it there costs between 3 and 7 kWh. [10] Assuming an average of 5 kWh this means a 127,035,000,000 GB multiplied by 5kWh, resulting in a total energy expenditure of 635,175,000,000 kWh. Depending on the energy generation used, CO2 emissions vary. We are using a global average of 0,475 kgCO2 / 1 kwH. [11] In total this means that there will be 301,708,125,000 KG of CO2, or roughly 301 million metric tons of CO2 produced to transfer data to the cloud and store it there in 2020. 

Projections for 2025 have data volumes as high as 79.4 ZB. [12] Following the same calculations as above, IoT devices would be responsible for 8 billion metric tons of CO2 in 2025.* We estimate that using ObjectBox can cut CO2 caused by data transmission and data centers by 50-90%, by keeping the majority of data on the device, and transmitting data efficiently. It will take time for ObjectBox to enter the market, so assuming a 10% market saturation by 2025 and an average energy reduction of 70%, using ObjectBox could cut projected CO2 emissions by 594 million metric tons in 2025.

ObjectBox is on a mission to reduce digital waste which unnecessarily burdens bandwidth infrastructure and fills cloud servers, forcing the expansion of cloud farms and in turn, contributing to the pollution of the environment. As our digital world grows, we all need to give some thought to how we should structure our digital environments to optimize and support useful, beneficial solutions, while also keeping them efficient and sustainable. 

*Of course, in that time, the technologies will all be more efficient and thus use less electricity while at the same time CO2 emissions / kWh will have dropped too. Thus, we are aware that this projection is an oversimplification of a highly complex and constantly changing system.

[1] https://www.theclimategroup.org/sites/default/files/archive/files/Smart2020Report.pdf
[2] https://www.iea.org/reports/tracking-buildings/data-centres-and-data-transmission-networks
[3]“Data centres… have eaten into any progress we made to achieving Ireland’s 40% carbon emissions reduction target.” from https://www.climatechangenews.com/2017/12/11/tsunami-data-consume-one-fifth-global-electricity-2025/
[4] https://medium.com/stanford-magazine/carbon-and-the-cloud-d6f481b79dfe
[5] https://www.researchgate.net/publication/323867714_The_carbon_footprint_of_distributed_cloud_storage
[6] https://www.resilience.org/stories/2020-01-07/the-invisible-and-growing-ecological-footprint-of-digital-technology/
[7] https://www.idc.com/getdoc.jsp?containerId=prUS45213219, https://priceonomics.com/the-iot-data-explosion-how-big-is-the-iot-data/, https://www.gartner.com/en/newsroom/press-releases/2018-11-07-gartner-identifies-top-10-strategic-iot-technologies-and-trends, https://www.iotjournaal.nl/wp-content/uploads/2017/02/white-paper-c11-738085.pdf, ObjectBox research
[8] Forrester (https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Preventing-IoT-data-waste-with-the-intelligent-edge), Harvard BR (https://hbr.org/2017/05/whats-your-data-strategy), IBM (http://www.redbooks.ibm.com/redbooks/pdfs/sg248435.pdf), McKinsey (https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world)
[9] https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/
[10] According to the American Council for an Energy-Efficient Economy: 5,12 kWh of electricity / GB of transferred data. According to a Carnegie Mellon University study: 7 kWh / GB. The American Council for an Energy-Efficient Economy concluded: 3.1 kWh / GB.
[11] https://www.iea.org/reports/global-energy-co2-status-report-2019/emissions
[12] https://www.idc.com/getdoc.jsp?containerId=prUS45213219

Time Series & Objects: Using Data on the Edge

Time Series & Objects: Using Data on the Edge

Many IoT projects collect, both time series data and other types of data. Typically, this means they will run two databases: A time-series database and a traditional database or key/value store. This creates fracture and overhead, which is why ObjectBox TS brings together the best of both worlds in one database (DB). ObjectBox TS is a hybrid database: an extremely fast object-oriented DB plus a time-series extension, specially optimized for time series data. In combination with its tiny footprint, ObjectBox is a perfect match for IoT applications running on the edge. The out-of-the-box synchronization takes care of synchronizing selected data sets super efficiently and it works offline and online, on-premise, in the cloud.

time-series-data-example-temperature

What is time series data?

There are a lot of different types of data that are used in IoT applications. Time-series is one of the most common data types in analytics, high-frequency inspections, and maintenance applications for IIoT / Industry 4.0 and smart mobility. Time series tracks data points over time, most often taken at equally spaced intervals. Typical data sources are sensor data, events, clicks, temperature – anything that changes over time.

Why use time series data on the edge?

Time-series data sets are usually collected from a lot of sensors, which sample at a high rate – which means that a lot of data is being collected.

For example, if a Raspberry Pi gateway collects 20 data points/second, typically that would mean 1200 entries a minute measuring e.g. 32 degrees. As temperatures rarely change significantly in short time frames, does all of this data need to go to the cloud? Unless you need to know the exact temperature in a central location every millisecond, the answer is no. Sending all data to the cloud is a waste of resources, causing high cloud costs without providing immediate, real-time insights.

time-series-edge

The Best of Both Worlds: time series + object oriented data persistence

With ObjectBox you aren’t limited to only using time series data. ObjectBox TS is optimized for time series data, but ObjectBox is a robust object oriented database solution that can store any data type. With ObjectBox, model your world in objects and combine this with the power of time-series data to identify patterns in your data, on the device, in real time. By combining time series data with more complex data types, ObjectBox empowers new use cases on the edge based on a fast and easy all-in-one data persistence solution. 

Bring together different data streams for a fusion of data; mix and match sensor data with the ObjectBox time series dashboard and find patterns in your data. On top, ObjectBox takes care of synchronizing selected data between devices (cloud / on-premise) efficiently for you.

time-series-data-visualization-dashboard

Get a complete picture of your data in one place

Use Case: Automotive (Process Optimization)

While most manufacturers, whether they’re producing cars, the food industry, or utilities, have been optimizing production for a long period of time. However, there are still many cases and reasons why costly manual processes prevail.  One such example is automotive varnish. In some cases, while the inspection is automatic and intelligent, a lot of cars need to be touched up by hand, because the factors leading to the errors in the paint are not yet discovered. While there is a lot of internal expert know-how available from the factory workers, their gut feel is typically not enough to adapt production processes.

How can this be improved using time series and object data? 

The cars (objects) are typically already persisted including all the mass customization and model information. If now, all data, including sensor data, of the manufacturing site like temperature, humidity, spray speed (all time-series data) is persisted and added to each car object, any kind of correlations between production site variables, individual car properties and varnish quality can be detected. The gut feel of the factory workers giving a great starting point for Quick Wins in the analysis and detecting patterns before more long term effects and AI / automatic learning kicks in to optimize the factory setup best possible to reduce the need for paint touch ups as much as possible. 

Use Case: Smart Grids

Utility grid loads shift continually throughout the day, effecting grid efficiency, pricing, and energy delivery. Using Smart Grids, utilities companies can increase efficiency and reliability in real time. In order to get insights from Smart Grids, companies need to collect a large volume of data from existing systems. A huge portion of this data is time series, e.g. usage and load statistics. On top, they incorporate other forms of data, e.g. asset relationship data, weather conditions, and customer profiles. Using visualization and analytical tools, these data types can be brought together to generate business insights and actionable operative goals.

ObjectBox TS: time series with objects

Storing and processing both time series data and objects on the edge, developers can gather complex data sets and get real time insight, even when offline. Combining these data types gives a fuller understanding and context for data – not only what happens over time, but what other factors could be influencing results. Using a fast hybrid edge database allows developers to save resources, while maintaining speed and efficiency. By synchronizing useful data to the cloud, real time data can be used for both immediate action, and post-event analysis.

Get in touch with our team to get a virtual demo of ObjectBox TS, or check out the sample GitHub repo to see more about the code.

Introducing: ObjectBox Generator, plus C++ API [Request for Feedback!]

Introducing: ObjectBox Generator, plus C++ API [Request for Feedback!]

We are introducing the ObjectBox Generator today to simplify ObjectBox development for more programming languages, starting with C/C++. Additionally, we are releasing a brand new C++ API that goes hand in hand with the new generator. Historically, our C API was rather low level as it was focused on providing the foundation for our Swift and Go APIs. With this release we want to provide C/C++ developers with ObjectBox convenience and ease of use. 

ObjectBox Generator takes over the burden of writing the binding code and data model declaration. Based on a single input file, it generates the code for you, so you can focus on the actual application logic.

Generator Example

ObjectBox let’s you handle data as FlatBuffers. For example, you can put and get data objects as FlatBuffers encoded bytes. To work with FlatBuffers, you need to define a FlatBuffer schema file (.fbs). And this file is also the input for ObjectBox Generator. This way, everything is defined in a single location.

Let’s say we have a FlatBuffers schema file “task.fbs” with the following content:

Now, we can tell ObjectBox Generator to use this file to generate C++ sources:

This makes ObjectBox Generator to generate the following files:

  • objectbox-model.h: source code to build the internal data model, that you need to pass when creating a store.
  • objectbox-model.json: keeps track of internal schema IDs; you don’t need to worry about this except that you should put it in your source control.
  • task-cpp.obx.h: the C++ value structs (data objects), binding code for FlatBuffers and the new Box class.

C++ API Example

Now, let’s use the previously generated code and the new C++ API around the Store and Box classes. A simple CRUD application boils down to a few lines:

Note that the generated code is header-only and compatible with the existing ObjectBox C-API, allowing both to be used from the same application. The C and C++ APIs both have unique advantages: the C++ API uses RAII so you do not need to worry about cleaning up, while the C API has additional features, e.g. queries.

Open Source, Docs

ObjectBox Generator is open source and available on GitHub. The repository comes with a readme file that also serves as a documentation. Among other things, you will find ObjectBox specific annotations there, which are used in fbs files to express ObjectBox-specific concerns. For example, in the definition of Task above, we used ulong as a FlatBuffers type to store dates. However, FlatBuffers does not know what a date is and we use ObjectBox annotations to express this:

For our initial release of ObjectBox Generator and the public C++ API we decided on labeling it as version 0.9. Although we are already very close to a 1.0 and we wanted to gather some feedback before our first major release. As we can still change the API or smooth out any rough edges you may find, we cannot stress enough how much we welcome and appreciate your feedback at this point. Thank you!

Why Edge Computing is More Relevant in 2020 Than Ever

Why Edge Computing is More Relevant in 2020 Than Ever

The world has recently been forced to digitize – both more quickly and to a greater extent; coronavirus has created the need to remodel how work, socializing, production, entertainment, and supply chains function. Despite decades of digitization efforts, with the pandemic upon us, digitization challenges have become transparent. Many companies and countries realize now, they have fallen behind. And those that have not yet digitized are hit hardest by the pandemic. [1] With people leaning heavily on online digital solutions, internet infrastructure is at its capacity limit. [2] Accordingly, users are seeing broadband speeds drop by as much as half. [3] In Europe, governments even requested to reduce the quality of Netflix, Amazon Prime, Youtube and other streaming services to improve network speeds. [4]

These challenges bring to light the growing need for an alternative to cloud computing. Cloud computing is an inherently centralized computing paradigm. Edge Computing helps overcome many of the disadvantages of centralized computing. Edge Computing is inherently decentralized and keeps data local, at the ‘edge’ of the network. Edge Computing is ideal for both, data-intensive content and latency-sensitive applications. Edge Computing makes efficient use of local data and reduces the amount of traffic in the network.

Coronavirus accelerates the need to digitize

It was clear even before the outbreak that internet infrastructure was struggling to keep up with growing data volumes. However, the pandemic has made broadband limitations more apparent to everyday users.

Projections estimate that by 2025 there will be 20 million IoT devices [5] and 1.7MB of data created per second per person. It is slow, expensive, and wasteful to send all of this data to the cloud for storage and processing. This practice overburdens bandwidth and data center infrastructure. It makes projects expensive and unsustainable. Working with the data, locally, on the edge, where it was produced and is used, is more efficient than sending everything to the cloud and back. It brings reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data and heightened data privacy – and even reduces CO2. Indeed, prior to the pandemic, edge computing was on the strategic roadmap for over 50% of mobility decision makers. [6]

As the world begins to recover from the coronavirus pandemic, digitization efforts will no doubt increase. We will see intelligent systems implemented across industries and value chains, accelerating innovation and alongside: data volumes and subsequent strain on network bandwidth. Edge computing is a key technology to ensure that this digitalization is both scalable and sustainable.  

Edge Computing takes the ‘edge’ off bandwidth strain

What is Edge Computing

Today, over 90% of enterprise data is sent to the cloud to be stored and processed. By 2025, this number will drop to just 25%. [7] The remaining data is stored and used on the device it was created on. This is called edge computing. It entails that data is stored and used locally, on the “edge” of the network, e.g. a smart phone or IoT device. Edge computing delivers faster decision making, local and offline data processing, as well as reduced data transfer to the cloud (e.g. filtered, computed, extra- or interpolated data), which saves both bandwidth and cloud storage costs. 

The Edge complements the Cloud

Although some might set cloud and edge in competition, the reality is that edge computing and cloud computing are both useful and relevant technologies. Both have different strengths and ideal use cases. Together they can provide the best of both worlds: decentralized local storage and processing, making efficient use of hardware on the edge and central storing and processing of some data, enabling additional centralized insights, data backups (redundancy), and remote access. To combine the best of both worlds, relevant and useful data must be synchronized between the edge and cloud in a smart and efficient way.  

Edge computing is an ideal technology to reduce the strain on data centers, so those functions that need cloud connection have adequate bandwidth; while those use cases that benefit from reduced latency and offline functionality are optimized on the edge.

The Edge: interface between the Physical and the Digital World

Edge devices handle the interface between the physical world and the cloud, enabling a whole set of new use cases. “Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs”. [8] And therefore need to happen locally, on the edge. We may see edge computing enabling new forms of remote engagement [9], particularly in a post-corona environment.

Edge devices can be anything from a thermostat or small sensor to a fridge or mobile phone or car – and they are part of our direct physical world and use data from their local environment to enable new use cases. Think self-stocking fridges, self-driving cars, drone-delivered pizzas. In the same way, Edge Computing is the key to the first real world search engine. I am waiting for it every day: “Hey Google, where are my keys?” Within a location like a house, the concepts and technologies to enable such a real-world search engine are all clear and available – it is just a matter of time and ongoing digitization. The basis will need to be a fast and sustainable edge infrastructure. 

Sustainability on the Edge

Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. [10] While data centers are seeing a positive trend towards using green data centers, an even more sustainable approach is to cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Edge Computing strategies that harness the power of already deployed available hardware (like e.g. smartphones, machines, desktops, gateways) make the solution even more sustainable.

Intelligent Edge: AI and Edge advance hand in hand

The growth of Artificial Intelligence (AI) and the Edge will go hand in hand. As more and more data is generated at the edge of the network, there will be a greater demand for intelligent data processing and structured optimization to reduce raw data loads going to the cloud. [11] Edge AI will have the power to work with data on local devices, keeping data streams more useful and usable. In the near future, Machine Learning applications will have the ability to learn and create unique, localized, decentralized insights on the edge – based on local inputs.

“With Edge AI, personalization features that we want from the app can be achieved on device. Transferring data over networks and into cloud-based servers allows for latency. At each endpoint, there are security risks involved in the data transfer”. [12] Which is part of the reason why the Edge AI Software market is forecasted to reach 1.12 trillion dollars volume by 2023. The development of AI accelerators, which improve model inferencing on the edge, namely from NVIDIA, Intel and Google are helping to make AI on the edge more viable. [13] A fast edge database is a necessary base technology to enable more AI on the edge. 

Edge Computing – an answer to Data Privacy concerns and a need for Resilience

As data collection grows in both breadth and depth, there is a stronger need for data privacy and security. Edge computing is one way to tackle this challenge: keeping data where it is produced, locally, makes data ownership clear and data less likely to be attacked and compromised. If compromised, the data compromised is clearly defined, making notification and subsequent actions manageable. ObjectBox, in its core and as an edge technology, is designed to keep data private, on those devices it was created on, and only share select data as needed. 

The more our private and working lives as well as the larger economy depend on digitalization, the more important it is that systems, underlying computing paradigms as well as networks have strong resilience and security. In computer networking, resilience is the ability to “provide and maintain an acceptable level of service in the face of faults and challenges to normal operation.” [14]

 storing as much data as possible on the  0 01.05.2020Edge Computing shifts computer workloads – the collection, processing, and storage of data – from central locations (like the cloud) to the edge of the networks to many individual devices such as cell phones. Accordingly, any strain is distributed to many devices. Therefore, the risk of a total breakdown is reduced: If one device does not work anymore, the rest is still working. Depending on the setup, the individual devices could even compensate for devices that have a problem.

The same applies to security risks: Even if data from one device is compromised, all other data sets are still safe; the loss is thus very limited and clear.  Overall, as a complement to the cloud, edge computing provides improved strength and security in local networks around the world. These local infrastructures can relieve the pressure on the existing complex dependencies, and in turn make the wider system more resilient and flexible. With Edge Computing crisis response can therefore in all likelihood be faster, better informed, and more effective. [15]

Why Corona-Tracking-Apps need to work on the edge

There has been quite some debate about taking a centralized versus decentralized approach to Corona-Tracking-Apps. [16] Many people are rightly worried about their data. Edge Computing – storing most parts of the data locally, on the user’s device – could be a great way to avoid unnecessary data sharing and keeping data ownership clear. At the same time, data would be by and large much more secure and less likely to be attacked and hacked, as the data to be gained is very reduced. An intelligent syncing mechanism then takes care that the data which needs to be shared, is shared in a selective, transparent and secure way.

UPDATE 01.05.2020: The German government changed its initial decision and will now be using a decentralized approach, storing as much data as possible on the edge, for the Corona-Tracking-App.

The next few years will see big cultural changes in both our personal and professional lives – a portion of those changes will be driven by increased digitalization. Edge computing is an important paradigm to ensure these changes are sustainable, scalable, and secure. Ultimately, we have the chance to rise from this crisis with new insights, new innovation, and a more sustainable future.

1. https://www.netzoekonom.de/2020/04/11/die-oekonomie-nach-corona-digitalisierung-und-automatisierung-in-hoechstgeschwindigkeit/
2. https://www.cnet.com/news/coronavirus-has-made-peak-internet-usage-into-the-new-normal/
3. https://www.nytimes.com/2020/03/26/business/coronavirus-internet-traffic-speed.html
4. https://www.theverge.com/2020/3/27/21195358/streaming-netflix-disney-hbo-now-youtube-twitch-amazon-prime-video-coronavirus-broadband-network
5. https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf
6. https://www.forbes.com/sites/forrester/2019/12/02/predictions-2020-edge-computing-makes-the-leap/#1aba50104201
7. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/
8. https://www.iotworldtoday.com/2020/03/19/ai-at-the-edge-still-mostly-consumer-not-enterprise-market/
9. https://www.accenture.com/us-en/insights/high-tech/edge-processing-remote-viewership
10. https://link.springer.com/article/10.1007/s12053-019-09833-8
11. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
12. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
13. https://www.forbes.com/sites/janakirammsv/2019/07/15/how-ai-accelerators-are-changing-the-face-of-edge-computing/#2c1304ce674f
14. https://en.wikipedia.org/wiki/Resilience_(network)
15. https://www.coindesk.com/how-edge-computing-can-make-us-more-resilient-in-a-crisis
16. https://venturebeat.com/2020/04/13/what-privacy-preserving-coronavirus-tracing-apps-need-to-succeed/

Why do we need Edge Computing for a sustainable future?

Why do we need Edge Computing for a sustainable future?

Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. While data centers are seeing a positive trend towards using green energy, an even more sustainable approach (alongside so-called “green data centers” [1]) is to cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Ideally, an Edge Computing strategy harnesses the power of already deployed available devices (like e.g. smartphones, machines, desktops, gateways), making the solution even more sustainable.

Why do Digitisation and IoT projects need to think about sustainability now?

Huge centralized data centres (cloud computing) have become a critical part of the infrastructure for a digitalized society. These large central cloud data centers produce a lot of carbon emissions, electric and electronic waste. [2] The share of global electricity used by data centres is already estimated to be around 1-3% [3] and data centers generate 2% of worldwide CO2 emissions (on par with the aviation industry). [4]

54% of which are caused by the cloud data centers of the big hyperscalers (Google, Amazon, Microsoft, Alibaba Cloud). [5] On top of this, providing and maintaining cloud infrastructure (manufacturing, shipping of hardware, buildings and lines) also consumes a huge amount of greenhouse gases [3] and produces a lot of abnormal waste (e.g. toxic coolants) at the end of life. [6]

Bearing that in mind, the growth forecasts for digitization, IoT, and Mobile [7] are concerning. The steady increase in data processing, storage, and traffic in the future, comes with a huge electricity demand for this industry. [8] In fact, estimations expect the communications industry to use 20% of all the world’s electricity by 2025. [9]

Shifting to green energy is a good step. However, a more effective and ultimately longer term solution requires looking at the current model of data storage, filtering, processing and transferal. By implementing Edge Computing, we can reduce the amount of useless and wasteful data traversing to and from the cloud as much as possible, thus reducing overall energy requirements in the long term.

What is Edge Computing?

While until recently 90 percent of enterprise data was sent to the cloud, this is changing rapidly. In fact, this number is dropping to only 25 percent in the next 3 years according to Gartner. By then, most of the data will be stored and used locally, on the device it was created on, e.g. on smartphones, cars, trains, machines, watches. This is called Edge Computing. Accordingly, edge devices need the same technology stack (just in a much smaller format) as a cloud server. This means: An operating system, a data storage / persistence layer (database), a networking layer, security functionalities etc. that run efficiently on restricted hardware.

As you can only use the devices’ resources, which can be pretty limited, inefficient applications can push a device to its limits, leading to slow response rates, crashes, and battery drain.

EDGE DEVICE ARCHITECTURE

Edge Computing is much more than some simple data pre-processing, which takes advantage of only a small portion of the computing that is possible on the edge. An on-device database is a prerequisite for meaningful Edge Computing. With an on-device database, data can be stored and processed on the devices directly (the so called edge). Only useful data is sent to the server and saved there, reducing the networking traffic and computing power used in data centers tremendously, while also making use of the computing resources of devices which are already in use. This greatly reduces bandwidth and energy required by data centers. On top, edge computing also provides the flexibility to operate independent from an Internet connection, enables fast real time response rates, and cuts cloud costs.

Why is Edge Computing sustainable?

Edge Computing reduces network traffic and cloud data center usage

With Edge Computing the amount of data traversing the network can be reduced greatly, freeing up bandwidth. Bandwidth refers to the transmission speed of data on the network. While maximum speeds are published by the network operators, the actual speed obtained in a given network is almost always lower, because the bandwidth is shared and limited. The more data transferred at any given moment, the slower the network speed. Data on the edge is also much more likely to be used, and then (due to restricted devices size) deleted when it is no longer useful.

Edge computing is optimized for efficiency

Edge “data centres” are typically more efficient than cloud data centres. As described above, resources on edge devices are restricted – as opposed to cloud infrastructure, edge devices do not scale horizontally. Therefore, every piece of the tech stack is – ideally – highly optimized for resource efficiency. Any computing done more efficiently helps reduce energy consumption, especially taking into account the huge number of devices already deployed (number), the worldwide impact is significant.

With Edge Computing you can put already deployed hardware to better use

On top, there is a realm of edge devices already deployed that is currently underused. Many existing devices are capable of fairly complex computing; when these devices send all of their data to the cloud, an opportunity is lost. Edge Computing utilizes existing hardware and infrastructure,  taking advantage of the existing computing power. If these devices continue to be underused, we will need to build bigger and bigger central data centers, simultaneously burdening existing network infrastructure and reducing bandwidth for senselessly sending everything to the cloud.

Cloud versus Edge: an Example

Today, many projects are built based on cloud computing. Especially in first prototypes or pilots, cloud computing offers an easy and fast start. However, with scale, cloud computing often becomes too slow, expensive, and unreliable. In a typical cloud setup, data is gathered on edge devices and forwarded to the cloud for computation and storage. Often a computed result is sent back. In this design, the edge devices are dumb devices that are dependant upon a working internet connection and a working cloud server; they do not have any intelligence or logic of their own. In a smart home cloud example, data would be sent from devices in the home, e.g. a thermostat, the door, the TV etc. to the cloud, where it is saved and used.

If the user would want to make changes via a cloud-based mobile app when in the house, the changes would be send to the cloud, changed there and then from there be sent to the devices. When the Internet connection is down or the server is not working, the application will not work.

With Edge Computing, data stays where it is produced, used and where it belongs – without traversing the network unnecessarily. This way, cloud infrastructure needs are reduced in three ways: Firstly, less network traffic, secondly, less central storage and thirdly less computational power. Rather, edge computing makes use of all the capable hardware already deployed in the world. E.g. in a smart home, all the data could stay within the house and be used on site. Only the small part of the data truly needed accessible from anywhere would be synchronized to the cloud.

Take for example a thermostat in such a home setting: it might produce 1000s of temperature data points per minute. However, minimal changes typically do not matter and data updates aren’t necessary every millisecond. On top, you really do not need all this data in the cloud and accessible from anywhere.

With Edge Computing, this data can stay on the edge and be used within the smart home as needed. Edge Computing enables the smart home to work fast, efficiently, and autonomous from a working internet connection. In addition, the smart home owner can keep the private data to him/herself and is less vulnerable to hacker attacks. 

How does ObjectBox make Edge Computing even more sustainable?

ObjectBox improves the sustainability of Edge Computing with high performance and efficiency: our 10X speed advantage translates into less use of CPU and battery / electricity. With ObjectBox, devices compute 10 times as much data with equivalent power. Due to the small size and efficiency, ObjectBox runs on restricted devices allowing application developers to utilize existing hardware longer and/or to do more instead on existing infrastructure / hardware.

Alongside the performance and size advantages, ObjectBox’ Sync solution takes care of making data available where needed when needed. It allows synchronization in an offline setting and / or to the cloud. Based on efficient syncing principles, ObjectBox Sync aims to reduce unnecessary data traffick as much as possible and is therefore perfectly suited for efficient, useful, and sustainable Edge Computing. Even when syncing the same amount of data, ObjectBox Sync reduces the bandwidth needed and thus cloud networking usage, which incidentally reduces cloud costs.

Also coming soon ObjectBox time series which will provide users an intuitive dashboard to see patterns behind the data. It will further help users to track thousands of data points/second in real-time

How Edge Computing enables new use cases that help make the world more sustainable

As mentioned above, there are a variety of IoT applications that help reduce waste of all kinds. These applications can have a huge impact on creating a more sustainable world, assuming the applications themselves are sustainable. Three powerful examples to demonstrate the huge impact IoT applications can have on the world:

1) Smart City Lighting: Chicago has implemented a system which allows them to save approx. 10 million USD / year and London estimates it can save up to 70% of current electricity use and costs as well as maintenance costs through smart public lighting systems. [10]

2) Reducing Food Waste: From farm to kitchen, IoT applications can help to reduce food waste across the food chain. Sensors used to monitor the cold chain, from field to supermarket, can ensure that food maintains a certain temperature, thus guaranteeing that products remain food safe and fresh longer, reducing food waste.

3) Reduce Water Waste: Many homes and commercial building landscapes are still watered manually or on a set schedule. This is an inexact method of watering, which does not take into account weather, soil moistness, or the water levels needed by the plant. Using smart IoT water management solution, landscape irrigation can be reduced, saving water and improving landscape health.

These positive effects are all the more powerful when the IoT applications themselves are sustainable. 

The benefits of cloud computing are broad and powerful, however there are costs to this technology. A combination of green data centers and Edge Computing helps to resolve these often unseen costs. With Edge Computing we can reduce the unnecessary use of bandwidth and server capacity (which comes down to infrastructure, electricity and physical space) while simultaneously taking advantage of underused device resources. ObjectBox amplifies these benefits, with high performance on small devices and efficient data synchronization – making edge computing an even more sustainable solution.