IoT, Edge Computing, and Digitalization in Healthcare

IoT, Edge Computing, and Digitalization in Healthcare

COVID-19 accelerated the digitization of healthcare, contributing to growing IoT adoption and exploding health data volumes. This digital transformation helps to improve efficiency and reduce costs, while opening new avenues for enhanced patient experience and well-being. Simultaneously, growing data privacy concerns, increasing costs, and heavier regulatory requirements are challenging the use of cloud computing to manage this data. A megashift to Edge Computing is addressing these challenges enabling a faster, safer and more reliable digital healthcare infrastructure.

The digital healthcare market 2020 and beyond, a high speed revolution

Prior to COVID, growth in digital health adoption stalled. [1] However, digitalization in the healthcare industry has sky-rocketed since the start of the pandemic. Reflecting this market turnaround, the third quarter of 2020 was a record year for investments in healthcare companies. [2] A trend that will continue in the next years, as analysts predict rapid growth across digital healthcare market sectors:

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Drivers of growth and change in digital healthcare

Digital Healthcare Growth Driver 1: COVID

The COVID pandemic accelerated the digitization of healthcare, pushing doctors, patients – and their data – to the virtual world. [8] The year 2020 marks the tipping point for digital healthcare offerings. With healthcare providers and patients forced to use digital means, adoption barriers have been removed for good. Indeed, a recent study from Forrester indicates that 36% of adults found that the care they received virtually was just as effective as what they would have received in person, and over 30% of adults will seek virtual care again in the future. [9]

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Over 30% of adults will seek
virtual care again in the future

Digital Healthcare Growth Driver 2: Growing Medical IoT Device Adoption

There will be a projected 55 billion IoT devices by 2025. [10] Internet of Medical Things (IoMT) are hardware devices designed to process, collect, and/or transmit health related data via a network. IoMT devices are projected to make up 30% of the entire IoT device market by 2025. [11] According to Gartner, 79% of healthcare providers are already using IoT in their processes, [12] i.e. remote health monitoring via wearables, ingestible sensors, [13] disinfection robots, [14] or closed-loop insulin delivery systems.15 IoMT devices increase safety and efficiency in healthcare, and future technical applications, like smart ambulances or augmented reality glasses that assist during surgery, are limitless.

IoMT devices are projected to make up
30% of the IoT device market by 2025

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Digital Healthcare Growth Driver 3: The Explosion of Health Data

Growing IoMT adoption is subsequently driving a rapid increase in the amount of collected health data. According to an IDC study, healthcare data is growing exponentially projected 36% CAGR through 2025; health data is expected to eclipse data volumes from sectors like manufacturing, financial services, and media. [16] The increase in healthcare data opens up new opportunities to apply technology to improve healthcare like e.g. big data analysis, AI and ML. In fact, the healthcare analytics market is expected to reach $84.2 billion by 2027 with a 26% CAGR. [17]

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Healthcare data will experience a
36% CAGR through 2025

Digital Healthcare Growth Driver 4: Technological innovations: Edge Computing, AI, and VR

Big health data sets are being used to revolutionize healthcare, bringing new insights into fields like oncology,18 and improving patient experience, care, and diagnosis: “Taken together, big data will facilitate healthcare by introducing prediction of epidemics (in relation to population health), providing early warnings of disease conditions, and helping in the discovery of novel biomarkers and intelligent therapeutic intervention strategies for an improved quality of life.” [19] In a November 2020 survey from Intel, 84% of healthcare providers shared that Artificial Intelligence deployments had occurred or were planned in their clinical workflow, an increase from 37% in 2018. This is unsurprising, as AI technologies are predicted to save the healthcare industry up to $150 billion per year, by answering “20 percent of
unmet clinical demand.” [20]

Augmented and Virtual Reality are also finding a place in healthcare settings. VR tools have been shown to reduce pain, [21] and are being used in therapy as a means to help patients overcome painful and traumatic experiences. Experts expect a realm of future AR applications in the operating room, assisting doctors during surgical procedures.

Current or planned AI deployments are at
84% in 2020, up from 37% in 2018

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Digital Healthcare Growth Driver 5: Underlying Social Megatrends

The global population is growing; global life expectancy is rising. Accordingly, by 2030 the world needs more energy, more food, more water. Explosive population growth in some areas versus declines in others contributes to shifts in economic power, resource allocation, societal habits and norms. Many Western populations are aging rapidly. E.g. in America, the number of people 65+ is expected to nearly double to 72.1 million by 2034. Because the population is shrinking at the same time, elder care is a growing challenge and researchers are looking to robots to solve it. [22]

Health megatrends focus not only on the prevention of disease, but also on the perception of wellness, and new forms of living and working. Over the coming decade more resources will be spent on health and longevity, leading to artificially and technologically enhanced human capabilities. More lifestyle-related disorders and diseases are expected to emerge in the future. [22]

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A focus on health and longevity will
lead to artificial & tech enhanced
human capabilities

The Challenges of Healthtech

Along with more data, more devices and more opportunity also comes more
responsibility and more costs for healthcare providers.

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Data Volume and Availability With the growing number of digital healthcare
and medical devices, a dazzling volume of health data is created and collected across many different channels. It will be vital for the healthcare industry to reliably synchronize and combine data across devices and channels. [23] Due to the sheer volume, reliable collection and analysis of this data is a major challenge. After it’s been processed, data needs to be available on demand, i.e. in emergency situations that require reliable, fast, available data.

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Reliability, Privacy, and Data Security are extremely important in health
technology; 70% of healthcare consumers are concerned about data privacy. [24] Data use is often governed by increasingly strict national regulation, i.e. HIPAA (USA) and / or GDPR (Europe). [25] With the number of cyber-attacks in the healthcare industry on the rise, [26] healthcare professionals must be even more diligent about the storage and processing of data. In addition, healthtech must be extremely well vetted; failures can cost lives – typical “banana products”, which ripen with the customers, are a no-go.

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IT Costs Medical devices contribute a large portion to healthcare budgets.
However as data volumes grow, data costs will also become a relevant cost
point. Sending all health data to the cloud to be stored and processed is not
only slow and insecure, it is also extremely costly. To curb mobile network and
cloud costs, much health data can be stored and processed at the edge, on
local devices, with only necessary data being synced to a cloud or central
server. By building resilient data architecture now, healthcare providers (e.g.
hospitals, clinics, research centers) can avoid future costs and headache.

Edge Computing is Integral to Data-driven Healthcare Ecosystems

With big data volumes, industries like healthcare need to seek out resilient information architectures to accommodate growing numbers of data and devices. To build resilient and secure digital infrastructure, healthcare providers will need to utilize both cloud computing and edge computing models, exploiting the strengths of both systems.

Cloud & Edge: What’s the Difference?

Cloud Computing information is sent to a centralized data center, to be stored, processed and sent back to the edge. This causes latency and higher risk of data breaches. Centralized data is useful for large scale data analysis and the distribution of data between i.e. hospitals and doctors’ offices.

Edge Computing Data is stored and processed on or near the device it was created on. Edge Computing works without an internet connection, and thus is reliable and robust in any scenario. It is ideal for time sensitive data (real time), and improved data privacy and security.

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Edge Computing contributes to resilient and secure healthcare data systems

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Transforming Healthcare with Edge Computing

Use Case: Secure and Up to Date Digital Record Keeping in Doctors Offices

For private doctors offices, embracing digitalization comes with different hurdles than larger healthcare providers. Often, offices do not keep a dedicated IT professional on staff, and must find digital solutions that serve their needs, while allowing them to comply with ever-increasing data regulations. As an industry used to legislative challenges, GPs know that sensitive patient data must be handled with care.

Solution providers serving private doctors offices are using edge databases to help keep patient data secure. An edge database allows private GPs to collect and store digital data locally. In newer practice setups, doctors use tablets, like iPads, throughout their practice to collect and track patient data, take notes and improve flexibility. This patient data should not be sent or stored in a central cloud server as this increases the risk of data breaches and opens up regulatory challenges. In a cloud-centered set up, the doctor also always needs to rely on a constant internet connection being available, making this also a matter of data availability

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Accordingly, the patient data is stored locally, on the iPads, accessible only by the doctor treating the patient. Some of the data is synchronized to a local, in-office computer at the front desk for billing and administration. Other data is only synchronized for backup purposes and encrypted. Such a setup also allows synchronizing data between iPads, enabling doctors to share data in an instant.

Use Case: Connected Ambulances – Real Time Edge Data from Home to Hospital

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Between an incidence location and the hospital, a lot can happen. What if everything that happened in the ambulance was reliably and securely tracked and shared with the hospital, seamlessly? Already there are trials underway using 5G technology to stream real time data to hospitals, [27] and allowing ambulance medics to access patient data while in transit. [28] Looking to the future, Edge Computing will enable digital healthcare applications to function in realtime and reliably anywhere and anytime, e.g. a moving ambulance, in the tunnel, or a remote area, enabling ambulance teams and doctors to give the best treatment instantly / on-site, while using available bandwidth and networks when available to seamlessly synchronize the relevant information to the relevant healthcare units, e.g. the next hospital. This will decrease friction, enhance operational processes, and improve time to treatment.

Digital Healthcare: Key Take-Aways

Digital healthcare is a fast-growing industry; more data and devices alongside new tech are empowering rapid advances. Finding ways to utilize growing healthcare data, while ensuring data privacy, security and availability are key challenges ahead for healthcare providers. The healthcare industry must find the right mix of technologies to manage this data, utilizing cloud for global data exchange and big data analytics, while embracing Edge Computing for it’s speed, security, and resilience.

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Underutilized data plays a major role in health-tech innovation, [29] data is the lifeline of future healthcare offerings; however, there is still much work to be done to improve the collection, management and analysis of this data.

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It’s all about data availability. Either in emergency situations, or simply to provide a smooth patient experience, data needs to be fast, reliable, and available: when you need it where you need it.

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Edge computing alongside other developing technologies like 5G, Augmented and Virtual Reality, Artificial Intelligence and Machine Learning will empower a new and powerful digital healthcare ecosystem.

ObjectBox provides edge data software, to empower scalable and resilient digital innovation
on the edge in healthcare, automotive, and manufacturing. ObjectBox’ edge database and
data synchronization solution is 10x faster than any alternative, and empowers applications
that respond in real-time (low-latency), work offline without a connection to the cloud,
reduce energy needs, keep data secure, and lower mobile network and cloud costs.

Sources
1. https://www.accenture.com/us-en/insights/health/leaders-make-recent-digital-health-gains-last
2. https://www.cbinsights.com/research-state-of-healthcare-q3-2020
3. https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare%C2%A0
4. https://www.grandviewresearch.com/industry-analysis/wearable-medical-devices-market
5. https://www.marketsandmarkets.com/PressReleases/iot-healthcare.asp
6. https://www.grandviewresearch.com/press-release/global-mhealth-app-market
7. https://www.globenewswire.com/news-release/2020/05/23/2037920/0/en/Global-Digital-Health-Market-was-Valued-at-USD-111-4-billion-in-2019-and-is-Expected-to-Reach-USD-510-4-billion-by-2025-Observing-a-CAGRof-29-0-during-2020-2025-VynZ-Research.html
8. https://www.sciencemag.org/features/2020/11/telemedicine-takes-center-stage-era-covid-19
9. https://go.forrester.com/blogs/will-virtual-care-stand-the-test-of-time-if-youre-asking-the-question-its-time-tocatch-up/
10. https://knowledge4policy.ec.europa.eu/foresight/topic/accelerating-technological-change-hyperconnectivity/hyperconnectivity-iot-digitalisation_en
11. https://mobidev.biz/blog/technology-trends-healthcare-digital-transformation
12. https://www.computerworld.com/article/3529427/how-iot-is-becoming-the-pulse-of-healthcare.html
https://www.gartner.com/en/documents/3970072
13. https://science.sciencemag.org/content/360/6391/915
14. http://emag.medicalexpo.com/disinfection-robots-against-covid-19/
15. https://www.theverge.com/2019/12/13/21020811/fda-closed-loop-insulin-system-software-diabetes-tandemcontrol-iq
16. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
17. https://www.prnewswire.com/news-releases/healthcare-analytics-market-worth-84-2-billion-by-2027–growingat-a-cagr-of-26-from-2020–pre-and-post-covid-19-market-opportunity-analysis-and-industry-forecasts-bymeticulous-research-301117822.html
18. https://www.nature.com/articles/s41437-020-0303-2
19. June 2019, https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0
20. https://www2.stardust-testing.com/en/the-digital-transformation-trends-and-challenges-in-healthcare
21. https://www.geekwire.com/2018/snowworld-melts-away-pain-burn-patients-using-virtual-reality-snowballs/
22. https://www.pwc.com/gx/en/government-public-services/assets/five-megatrends-implications.pdf
23. https://www2.stardust-testing.com/en/the-digital-transformation-trends-and-challenges-in-healthcare
24. https://www.accenture.com/_acnmedia/PDF-133/Accenture-Digital-Health-Tech-Vision-2020.pdf#zoom=40
25. https://www.lexology.com/library/detail.aspx?g=99b83b76-3f2f-4b23-a5c3-30ad576af369
26. https://www.medicaleconomics.com/view/cyberattack-threat-to-health-care-providers-on-the-rise
https://www.healthcareitnews.com/news/fbi-hhs-warn-increased-and-imminent-cyber-threat-hospitals
https://blogs.microsoft.com/on-the-issues/2020/11/13/health-care-cyberattacks-covid-19-paris-peace-forum/
27. https://www.vodafone.co.uk/business/5g-for-business/5g-customer-stories/connected-ambulance
28. https://www.digitalhealth.net/2019/04/london-ambulance-access-patient-data/
29. https://news.crunchbase.com/news/for-health-tech-startups-data-is-their-lifeline-now-more-than-ever/

ObjectBox Recognized as a Sustainable Profitable Tech Solution by the Solar Impulse Foundation

ObjectBox Recognized as a Sustainable Profitable Tech Solution by the Solar Impulse Foundation

ObjectBox is proud to be officially recognized as a sustainable and efficient solution by the Solar Impulse Foundation. Although we have self-identified as a #sustainabletech company since our induction, we’re proud to be recognized as an “efficient, clean and profitable solutions with a positive impact on environment and quality of life,” after taking part in an in-depth technical and business evaluation with the team at the Solar Impulse Foundation.

Empowering tech innovation

This label recognizes that ObjectBox empowers innovation with a highly efficient and sustainable technology. The Solar Impulse Efficient Label identifies sustainable tech solutions from around the world to help companies choose their tech stack responsibly.  

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UN Sustainable Development Goals

All Solar Impulse awardees contribute to one or several of the UN Sustainable Development Goals; ObjectBox received the globally recognized label for supporting three of the Solar Impulse focused initiatives: 

  • Affordable and Clean Energy: ObjectBox
  • Clean Water and Sanitation
  • Industry, Innovation and Infrastructure : ObjectBox
  • Sustainable Cities and Communities: ObjectBox
  • Responsible Consumption and Production

How is ObjectBox sustainable?

objectbox-local-data-sustainable

ObjectBox enables scalable and sustainable digitalization with a high performance edge database solution and synchronization solution. The ObjectBox database empowers local data storage, while ObjectBox Sync reduces unnecessary data traffic. ObjectBox is therefore ideally suited for efficient, useful, and sustainable Edge Computing. 

Comparing the transmission of the same data sets, ObjectBox saves 20-60% on transmission data volume. By combining delta syncing with efficient compression based on standard and proprietary edge compression methods to keep data small, ObjectBox can reduce device energy consumption and thus CO2 emissions for data transmissions.

As our digital world grows, we all need to do what we can to structure these digital environments in an efficient and sustainable way. ObjectBox helps reduce digital waste. Digital waste unnecessarily burdens bandwidth infrastructure and fills cloud servers, forcing the expansion of cloud farms and in turn, contributing to the pollution of the environment. Therefore, we are excited to be part of the 1000solutions program.

Dr. Vivien Dollinger

CEO and Co-founder, ObjectBox

What does it mean to get a Solar Impulse Label? 

The Solar Impulse Label: a label focused on both the environment and profitability

The first label to assess the economic profitability of products or processes that protect the environment. The Solar Impulse Efficient Solution Label is attributed following a strict selection process performed by external independent experts. By ensuring high standards of sustainability and profitability, this internationally recognized label is considered as a credible marker of quality for solution seekers in business and governments, facilitating their sourcing of solutions to reach environmental commitments.

About the Solar Impulse Foundation

The Solar Impulse Foundation aims to identify clean, efficient and profitable solutions in order to accelerate their implementation and the transition to a sustainable economy. Thanks to the awarding of a label with high standards of sustainability and profitability, the Foundation can support political and economic decision-makers in their efforts to achieve their environmental targets and encourage them to adopt more ambitious energy regulations, necessary for implementation at large-scale of these solutions on the market. A way to take the success of the first round-the-world solar flight further.

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Interesting in finding out how ObjectBox can make your edge computing project more sustainable?

What are Time Series Database Use Cases?

What are Time Series Database Use Cases?

What do self-driving cars, smart homes, autonomous stock/crypto trading algorithms, or energy sensor systems have in common? These applications are all based on a form of data that measures how things change over time. It’s called time-series data and it plays a very important role in our lives today.

Accordingly, time-series databases also became a hot topic.

time series database use cases

What is a time-series database?

A time-series database (TSDB) can be defined simply as a database optimized for storing and using time-stamped or time-series data. You don’t need to use a TSDB to work with time-series data. Any relational or NoSQL database or a key-value-store will do, e.g. MongoDB or redis. However, when dealing with time-series data (e.g. temperature, air pressure or car velocity data), a TSDB makes your life as a developer a hell of a lot easier.

Indeed, the two main reasons why TSDBs is the fastest-growing category of databases, are usability and scalability. A purpose-built time-series database typically includes common functions of time-series data analysis, which is convenient when working with time-series data. Because time-series data typically continually produces new data entries, data grows pretty quickly, and with high-frequency data or many time-series data sources, data ingestion quickly becomes a challenge. Time-series databases are optimized to scale well for time-series data with time being a common denominator and outperform any other database without specific time-series optimizations. This is why more and more people are adopting time-series databases and using them for a variety of use cases.

What are time-series database use cases?

Monitoring Use Case time series

Monitoring sensor data 

One of the use cases is the monitoring of sensor data for safety measurements, predictive maintenance, or assistance functions. E.g. a car stores and uses all kinds of sensor data like tyre pressure, surrounding temperature and humidity for driver assistance and maintenance support. An aircraft monitors gravity and aerodynamic principles to reassure pilots that everything is alright – or to alert them that something has gone wrong. In fact, a Boeing creates on average half a terabyte of data per flight, most of which is time-series data.  [1]

Logistics Use Case time series database

Tracking assets

Tracking assets is ideal for a time-series database as you constantly want to monitor where assets are, e.g. the cars of a fleet or any goods you might be stocking or shipping. These applications typically include unique vehicle or asset IDs, GPS coordinates, and additional metadata per timestamp. Apart from keeping track of the assets in realtime, you also can use the data for logistics and optimize e.g. your stocking and delivery processes.

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Analyzing and predicting shopping behavior

Or, many e-commerce systems store all information of an item from product inventory, logistics data and any available environmental data to transaction amount, all items of the shopping cart purchased, to payment data, order information etc. In this case, a TSDB will be used to collect these large amounts of data and analyze them quickly to determine e.g. what to recommend to customers to buy next or optimize the inventory or predict future shopping behavior.

What are the most popular time series databases?

Well, here is our list of popular / established time series databases to use in 2020 to get you started:

  • InfluxDB: an open-source time series database, written in Go and optimized for high-availability storage and retrieval of time series data for operations monitoring, application metrics, IoT sensor data, and real-time analytics
  • KairosDB: a fast distributed scalable time series database written on top of Cassandra. 
  • Kdb+:  is a column-based relational time series database with a focus on applications in the financial sector.
  • Objectbox TS: superfast object persistence with time-series data on the edge. Collect, store, and query time-series data on the edge and sync selective data to / from a central location on-premise or in the cloud as needed.
  • TimescaleDB: an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension with full SQL support.

For an overview of time-series databases currently available for productive use, see DB Engines. The database of databases is also a good resource if you are deeply interested in the database landscape; it is more extensive, but it includes any DB available independent of the level of support or if it is still maintained, also hobby projects. 

Time Series Database Use Cases

What do you do when you have more than just time-series data?

Typically, a time-series database is not well suited to model non-time-based data. Therefore, many companies choose to implement two databases. This increases overhead, disk space, and is especially impractical when you deal with edge devices. 

Time Series + Object-Oriented Data Persistence

Storing and processing both time series data and objects, developers can collect complex datasets and combine them with time-series data. Combining these data types gives a more complete understanding and context to the data – not just what happens over time, but also other factors that affect the results. 

The best option is a robust object-oriented database solution that lets you model your data as it reflects the factual use case / the real world in objects and on-top is optimized for time series data. You can model your world in objects and combine this with the power of time-series data to identify patterns in your data. If this is indeed a database optimized for restricted devices and Edge Computing, you can even use this data in real-time and on the device. By combining time series data with more complex data types, an object time-series edge database can empower new use cases on the edge based on a fast and easy all-in-one data persistence solution. 

Still have questions? Feel free to contact us here!

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[1] Time Series Management Systems: A Survey Søren Kejser Jensen, Torben Bach Pedersen, Senior Member, IEEE, Christian Thomsen

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

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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

Green IoT Solution

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

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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

How EV Charging Benefits from Edge Computing

How EV Charging Benefits from Edge Computing

Edge computing allows data to be stored and used on local devices. Integrating Edge Computing directly within electric vehicle charging infrastructure improves station usability and also allows for real-time energy management.

Car charging and electric vehicles

The era of electric vehicles (EV) is coming: Already one in every 250¹ cars on the road is electric. While it is uncertain when electric vehicles will overtake traditional combustion engine vehicles, electric is clearly the future. Car charging infrastructure is critical for electric vehicle expansion – and one of the largest bottlenecks to EV adoption. Range anxiety is still one of the primary concerns for potential EV customers,² and charging station proliferation is still far behind traditional gas stations.

EV charging

State of the electric vehicle charging Market

The electric vehicle charging infrastructure market is still very divided, with many players vying for this large-growth sector – some predictions forecast over 40% CAGR for the car charging infrastructure market in the coming years.³ Car manufacturers, gas & oil, OEMs, and utilities companies (e.g. Tesla, VW, BMW, Shell, GE, Engie, Siemens, ABB) are actively taking part in the development of the market, recognizing the need to support future EV customers and the huge growth potential. Startups in the space like EcoG, Wirelane, flexEcharge and Elli offer solutions that focus on accessibility, efficiency and improving end user experiences.

Why Car Charging Stations need Offline Capability (Edge Computing)

First, let’s look at the challenges a vehicle charging provider needs to solve from a basic data perspective: Customers interfacing with charging stations require an account linked with basic information and payment methods. In order to charge a car, the user needs to be verified by the charging station, and is often required to have a pre-booked charging slot. Typically, a user would create a new account via a website or mobile phone beforehand, but not on the spot at the car charging station. Also booking slots are handled via a mobile app or website. However, the car charging station needs this information to allow a car to be charged.

This is only the most basic necessity. In the future, charging stations will provide more services to users, e.g. identifying users preference like cost over speed of charging, or choosing to charge with green energy. 

Depending on where the car charging station sits, it can be offline more or less often, e.g. in France there are quite many electric car charging stations in the country site, where the connection is typically flaky – and might not be available for days. On the other hand, there are stations that reside within a parking house or hotel and use a fixed land line for connectivity. In the latter case, your uptime can be very consistent, but typically you cannot guarantee the car charging station will be connected.

If the charging station tries to access this data only when it needs it, because a car is trying to charge, it may or may not have an internet connection at the time and thus the likelihood of failure is rather high. Accordingly, any new information should be pushed to the car charging stations when a connection is available and stored on the station. The hardware of a car charging station is capable enough to hold a lightweight database and persist data as is needed and useful.

EV charging edge computing solution

Choosing a data persistence layer (database) over a simple caching ensures not only that no data is lost, but can also allow more processing to happen on the station and allows for autonomous reactions. In combination with edge synchronization, which enables persistence layers to synchronize between car charging stations (that share a data space), fast data persistence allows for efficient load balancing as well as easily updating the configurations of all car charging stations.

 

Smart Energy Load Management – the need for fast response on the Edge

Managing energy is one of the greatest challenges for EV infrastructure providers. The difficulty here is less about overall energy consumption increasing – rather managing, predicting and preparing for high-demand peaks. Imagine everyone needs charging during a large public event, or at charging stations during holiday travel times – peak demands like these need to be anticipated and planned for. The future with electric cars needs to balance demand with a combination of smart chargers, efficient energy grid management, Vehicle-to-Grid (V2G) solutions, and perhaps even on-site batteries at larger charging stations to improve time-to-charge and optimize for electricity prices.

EV charging edge computing solution

Edge computing will play an important role in providing real-time, accurate energy load control, necessary for maintaining grid stability, particularly in emergency situations.⁴ At charging stations where many EVs plug in, smart edge nodes can balance charge schedules in real-time, optimizing based on EV owner requirements without overloading local transformers.⁵  On a larger scale, smart energy meters can use real-time edge computing to shift energy quickly to high-demand locations, cutting energy from low-priority appliances, limiting charge speeds, or pulling excess energy from V2G networks.

Thinking about energy management, the conversation fluidly moves from EV charging infrastructure to thinking about smart mobility, utilities, and smart city infrastructure on a larger scale. Car charging systems will be complex, interconnected and will progress in alignment with other ongoing digitization efforts to create data drive infrastructure across cities and the world. Edge computing, and base technologies like ObjectBox that enable working on the edge, are important enablers to ensure that real-time computing can happen anywhere and digitization is affordable, scalable, and sustainable.