IoT is growing at a very rapid rate and with it the vast amount of data it produces. Handling these amounts of data is an unresolved challenge. Edge Computing could be part of the solution

According to Dave Evans of Cisco, in 2010 the number of IoT devices connected to the internet passed the world population, with a device to person ratio of 1.84.1 By 2020 there will be up to ten Web-connected devices per person, collectively producing over 40 zettabytes of data.2, 4 

The graph on the right shows the estimated number of IoT devices from 2015-2025, Statista Gmbh.5 

Downsides of pure cloud computing in IoT use cases

Today, most IoT devices constantly push all data generated to the cloud and use little of the on-device capacities. There are some downsides to that:


  1. Data security
    When data is constantly being sent from device to the cloud, the risk of the data being compromised is huge. “As a centralized resource out of users’ control, the cloud resents an ever-present opportunity to violate privacy.”3 
  2. Realtime requirement
    IoT applications have a “need for speed”.2 The response time, however, inevitably decreases as the distance between device and the place where data is stored and computed (in the cloud) increases.2
  3. Cloud costs
    Pushing, storing, and processing all data in the cloud is associated with high cloud costs. These costs increase as data volumes increase.15

  4. Wastefulness
    Our current bandwidth infrastructure does not support this rapid growth: “With global Internet traffic growing by an estimated 22% per year, the demand for bandwidth is fast outstripping providers’ best efforts to supply it”.Even worse, most data stored in the cloud is of no value to the company and never used.

That is why analysts predict data will move to the edge. 2, 6, 10, 11

What is edge computing?

While there are varying definitions, a simple pragmatic definition is: computing data close to where it is produced, at the edge of the network, instead of a central point far away.1, 9 More technically, it is decentralized data persistence that happens on or near the devices that produce the data.

Moving data to the edge does not mean that data will be solely stored on the edge instead of the cloud. There is just a shift to more data being processed on the edge and less data stored in the cloud.1

Advantages of Edge Computing

Storing and processing data locally on device (e.g. on the IoT gateway) has some advantages:


  1. Privacy and Security
    If you are working with sensitive personal data that is not needed centrally / on the cloud, it is easier to keep data secure by storing it where it belongs.18 This can also ease up on GDPR compliance.12

  2. Latency / Speed
    If the data is being stored and processed in a local database, then computing can be done in real time rather than having to communicate back and forth with the cloud for every interaction.5 

  3. Offline-capability
    The more you compute on the edge, the more your app is independent from a constant network connection.19

  4. Costs
    Because you only store the data that is needed centrally or the data you really want to backup, your cloud costs will go down.12

  5. Resourcefulness
    Storing and processing data on the edge and only sending out to the cloud what will be used and useful saves bandwidth and server space.18

Practical Examples

Currently the main focus industries for IoT Edge Computing are Smart Cities, Autonomous Vehicles; Drones, and Industrial IoT.17

A simple case for an IoT edge solution is wearable health monitors. They locally analyze data like heart rate or sleep patterns and provide recommendations without the need for a constant cloud connection.16 It makes sense to be able to get health recommendations in any situation, no matter if there is an internet connection available. Also, not every patient may want all his/her personal health data stored online.
IoT Edge Computing
Straightforward IoT use cases, which can only work with local data processing, are (semi-)-autonomous cars. Data needs to be processed in real-time and independent of a network connection as no one would like to crash, because of lagging. Also, edge computing enables cars to process more sensor data faster and find patterns.5 
There is a trend to move data to the edge

When you look around the internet, you will find many studies predicting the rise of edge computing, for example: Peter Livine, partner at Andreessen Horowitz, predicted “the end of cloud computing” in favor of edge computing. Livine believes that the bulk of processing will soon take place at the device level.13 Transparency Market Research (TMR) forecasted the global edge computing market will be worth US $13.3 billion by the end of 2022.14 The IDC’s Global IoT Decision Maker Survey showed that 43% of IoT decision makers want to build on edge computing.4

For us edge computing makes a lot of sense, because it is resourceful, efficient, and data stays where it belongs. That’s why we built ObjectBox. Everything else, we’ll see… 😉


ObjectBox is a data storage and data sychronization solution for IoT-devices (10x faster than any alternative, across devices from sensor to server, 1 million+ entities/second400kb native core, cross platform compatible, ACID-compliant). 


[1] Cisco (2011)
[2] Ieee (2018)
[3] Usenix (2015)
[4] IDC (2017)
[5] Data Makes Possible (2018)
[6] IDG (2017)
[7] Scientific American (2016)
[9] Gartner (2017)
[10] Business Insider (2016)
[11] Ieee (2016) – as always with predictions. However, talking to many CTOs of IoT companies, we see there definitely is a need for edge computing.
[12] IoT Agenda (2018)
[13] Andreessen Horowitz (2016)
[14] Transparency Market Research (2017)
[15] SysGen (2017)
[16] Gartner (2017)
[17] Bowery Capital (2016)
[18] Hubraum (2017)
[19] HPE (2018)