The amount of load and data generated on the cloud is also increasing because of increasing applications and systems moving to cloud, making it difficult to perform analytics and extract important insights. To deal with this challenge, enterprises are leveraging edge analytics. Read on to find how edge analytics accelerates cloud analytics.
Mobile devices, wearables, cameras and many other connected devices or better-called “devices on edge” in different organizations and enterprises, generate a huge amount of decentralized data. Moving this data to the cloud to derive various insights and perform further analytics on them seems a very good option, but there is also a huge dependency that adds to the headache if we have everything on the cloud.
Why not to put everything on the cloud
- Continuous consolidation and synchronizing of data on the cloud can drain resources.
- Maintaining a consistent connection to the cloud gets difficult.
- Costs attached to data transfer and data storage on cloud grow significantly over time.
- Delay-induced due to data transfer and processing on cloud put a restriction in providing near real-time analysis.
So can edge analytics replace cloud analytics completely?
Honestly, edge analytics is not here to replace cloud analytics completely, but it is here to complement cloud analytics by driving near real-time analytics as it is close to the data source. Let us see how edge analytics empower cloud analytics.
According to the market research firm IDC, around 45 percent of data will be stored, managed, analyzed and kept right where it was produced, at the edge. So, organizations with 100% total cost of operations on the cloud can leverage edge computing to reduce it to 60%.
- Cloud operations cost can be reduced by using a distributed edge computing architecture, where edge devices together process a critical operation, which a cloud device cannot process on its own, thereby reducing cloud dependency.
- A combined edge-to-cloud architecture is critical for any industrial success. For this, experts need to differentiate and define the real-time analytics to be run at various levels, including edge sensor, infrastructure machine, gateway, controller within on premise appliances and racks or in the cloud.
- We are seeing a tremendous growth in sensor technology. By combining the innovations of sensor technology with the reducing hardware costs, we can establish an edge-to-cloud paradigm. Sensors with processing units can help take critical actions in an inconsistent cloud environment and can later synchronize with the cloud. The required architecture can vary as per industry.
So overall, a well-defined edge-to-cloud architecture as per domain and data would be accelerating cloud computing.
RELATED BLOG IoT Gateways – Drivers for Fog Computing
How edge analytics work for all industries
Edge analytics benefit organizations where data insights are needed at the edge. Manufacturing, retail, smart cities, energy, utilities, transportation, and logistics segments are leading the way in deploying edge analytics.
Let us look at sectors that can benefit from edge computing & analytics:
Brick-and-mortar stores are rich with edge devices such as cameras, beacons, sensors, Wi-Fi networks etc. They are looking for competitive advantages that can help them beat eCommerce businesses, and real-time edge analytics can provide them just that. With edge analytics, sales data, images, coupons used, traffic patterns, and videos are created to provide unprecedented insights into consumer behavior. They have perfect infrastructure and devices to explore edge analytics. Moreover, the mobile devices of customers and data generated by store apps, make this number swell more.
Real-time insights are of prime importance since retail stores need to know their customers’ needs immediately when they enter the store to keep them in store. A recommendation or an offer coming after the customer has left store can be of no use. Identifying customers’ behavior data is something that requires heavy processing power on the cloud. Leveraging some processing at the edge like tracking items viewed, picked, and bought can be a good idea. Other than that, metadata can be sent to the cloud lake to get recommendations, offers, etc., keeping the entire process near to real-time. A distributed edge computing architecture can boost this up further.
Manufacturing is an industry that requires analytics and computing at the edge. Take an example, an average offshore oil rig has nearly 30,000 sensors. They measure gas emission, pressure, temperature, etc., continuously. Connecting these to cloud lake and deriving analysis will be too costly and time-consuming. A majority of this data is actually not required for analytics; hardly 1-3% of data is used for analysis after cleaning the data. It can bring tremendous advantageous if these edge devices knew what analysis needs to be performed and what data needs to be sent to the cloud, thus saving ample bandwidth. Embedding computing capability in the form of complex event processing (CPE), edge devices can filter out noisy data and collect only information that is deemed useful. In the absence of cloud, the distributed edge computing can process this data for analysis, take critical actions, and can later notify the cloud about the updates.
Another example is of a smart production line. We know that in a production line, each process is time bound. Every action has to be taken in line with production processes. Hence, it becomes important to derive analysis at the edge. Pointing out manufacturing defects or anomalies, badly printed stickers, packaging, etc., in real-time can be achieved using edge analytics.
Healthcare is another domain where we are seeing a huge surge in the number of connected devices. In the near future, a hospital room on an average will have 15 to 20 medical devices, a majority of which will be networked. A large hospital can have as many as 85,000 connected medical and IoT devices, putting a massive strain on the cloud network. Edge computing and analytics can reduce this burden to a great extent. Here again, real-time analytics will carry more importance than delayed analytics. For example, a clinician’s mobile device is the edge between the patient who is the data source and the cloud. A clinician treating a patient with a tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time. Patients no longer need to wait for analytics results, which may reduce their number of visits.
In addition, edge computing in healthcare offers another concept called collaborative edge. In a collaborative edge, geographically dispersed data can be fused by creating virtual shared views. This shared data is exposed to the users through some pre-defined interfaces, which edge devices can directly consume.
To sum this up, with edge computing practitioners and patients can get the best response times from the data that is generated and collected by healthcare facilities. As the healthcare sector is using more and more medical devices that are connected to a common network, edge computing is about to become a standard in health IT infrastructure.
Connected devices are becoming essential components for enterprises as they can drive significant connectivity and integration between systems and data. The increasing number of devices getting connected to each other generates a huge amount of data.
However, when it comes to leveraging the full potential of these connected devices and data, it is necessary to have a scalable and robust environment which allows faster processing of data between systems.
The fundamental concern is on how to efficiently manage this data, as any data loss or delay in processing of data from a connected ecosystem can cause critical damage to an enterprise’s workflow.
Role of IoT gateway edge analytics in data processing & management
IoT Gateway is the key to any IoT deployment. It is a bridge between IoT devices and cloud that enables remote control of the devices and machines. The increasing number of devices propels the requirement for IoT gateways to solve the data management issues with Edge Analytics.
Edge analytics with IoT Gateway allows data processing before it is transmitted to the cloud. The gateway collects all the data from the connected devices and executes necessary algorithms or rule engine on it and sends actionable commands to connected devices. The actions allow for response to be taken in real-time and also helps in self-healing mechanism during faults/errors.
In large enterprises, having multiple geographical spread, there are a huge number of connected devices and generated data. This heterogeneous data, distributed at different levels (Devices and machines ) have high latency in cloud transferring due to the uncontrolled data flow. Here, distributed edge analytics is the solution as it allows faster data transfer and processing, resulting in the reduction of latency.
AWS Greengrass is the best example for the edge analytics setup. It allows enterprises to run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. Greengrass ensures quick response of IoT devices at the time of local events, that reduces the cost of transmitting IoT data to the cloud.
How distributed edge analytics works in larger geographical areas
Let’s take an example of smart grids to understand the concept in-detail.
Smart grids are the combinations of smart meters, smart appliances, renewable energy resources, energy efficient resources, and substations. In a particular city area, the number of smart meters is equivalent to the number of households in that area. These AMI (Advanced Metering Infrastructure) continuously collects the energy consumption data and route it to the IoT gateways. The gateway enables edge analytics and then the processed data is rerouted to the cloud by the gateway.
As the number of AMI is high in a particular area, the number of gateways will be proportionately higher.
Merits of distributed edge analytics:
- Reduced data transfer latency
- Fast access to the faulty areas
- Quick functional recovery and self healing capabilities that brings resilience in the system
Distributed edge analytics also enables fast response to the cloud in case of faults and failures with Fog Computing so that the recovery time can be minimal. Let us understand how.
How fog computing works with smart grids for faster data processing
Fog computing is the combination of two key components of data processing, Edge and Cloud both. The idea of combining edge computing with more complex computing (cloud computing) results into more reliable and faster data processing.
As smart grid tech is increasing rapidly, fog computing is the best tool for the data and information processing between consumers, grid operators, and energy providers.
In the edge analytics concept, the gateways form a mesh network. The individual mesh network of a designated area creates Fog Nodes. Each fog node is connected to each other, resulting in a fog network of smart meters and IoT gateways in the larger setups. The combination of these fog nodes then allows distributed fog computing, which gives the benefit of fast and real-time data analysis in any large geographical area. This further enables faster fault response time.
Use case of smart grids in distributed edge analytics
eInfochips developed a solution in which gateways are being connected into a mesh network with peer-to-peer communication. Mesh and cluster of gateways enable high availability and reliabilityof the IoT deployment in smart grids. Clustering enables distributed edge analytics. These distributed edge nodes allow processing of data at the edge before transferring it to the cloud.
According to the market research data, fog computing market is growing with the attractive amount of cost annual growth rate (CAGR), 55.6% between 2017 and 2022 (MarketsandMarkets).
With our edge and fog computing expertise, we help the IoT solution providers to optimise their computing infrastructure by distributing load between the cloud and edge devices in an intelligent way through our ready-to-use dynamic rule engine or custom solutions.
How edge computing is poised to jump-start the next industrial revolution.
From travel to fitness to entertainment, we now have killer apps for many things we never knew we needed. Over the past decade, we’ve witnessed tremendous improvements in terms of democratizing data and productivity across the consumer world.
Building on that, we’re entering a new era of software-defined machines that will transform productivity, products and services in the industrial world. This is the critical link which will drive new scenarios at even faster rates of innovation. By 2020, the Industrial Internet of Things (IIoT) is expected to be a $225 billion market.
To jump-start the productivity engine of IIoT, real-time response is needed at the machine-level at scale and that requires an edge-plus-cloud architecture designed specifically for the Industrial Internet. From Google maps to weather apps, we’ve been experiencing the benefits of cloud and edge computing working together in our daily lives for quite some time.
But, what is edge? Edge is the physical location that allows computing closer to the source of data. Edge computing enables data analytics to occur and resulting insights to be gleaned closer to the machines. While edge computing isn’t new, it’s beginning to take hold in the industrial sector – and the opportunity is far greater than anything we’ve seen in the consumer sector, and here’s why:
Real-time data in a real-time world: The edge is not merely a way to collect data for transmission to the cloud. We are now able to process, analyze and act upon the collected data at the edge within milliseconds. It is the gateway for optimizing industrial data. And when millions of dollars and human lives are on the line, edge computing is essential for optimizing industrial data at every aspect of an operation.
Take windfarms for example. If wind direction changes, the edge software onsite would collect and analyze this data in real-time and then communicate to the wind turbine to adjust appropriately using an edge device, such as a field agent and connected control system, and successfully capture more kinetic energy. Because the data is not sent to the cloud, the processing time is significantly faster. This increases wind turbines’ production, and ultimately distributes more clean energy to our cities, increasing the value of the renewable energy space.
Big data, big trade-offs: The harsh and remote conditions of many industrial sites make it challenging to connect and cost-effectively transmit large quantities of data in real-time. We are now able to add intelligence to machines at the edge of the network, in the plant or field. Through edge computing on the device, we’re bringing analytics capabilities closer to the machine and providing a less expensive option for optimizing asset performance.
Consider the thousands of terabytes of data from a gas turbine. Sending this data to the cloud to run advanced analytics maybe technologically possible, but certainly too cost prohibitive to do a daily basis. Through edge computing, we can capture streaming data from a turbine and use this data in real-time to prevent unplanned downtime and optimize production to extend the life of the machine.
Today, only 3% of data from industrial assets is useable. Connecting machines from the cloud to the edge will dramatically increase useable data by providing greater access to high powered, cost effective computing and analytics tools at the machine and plant level.
Consider the fact that for years traditional control systems were designed to keep a machine running the same way day in and day out for the lifecycle of the machine. At GE Energy Connections, we recently debuted the Industrial Internet Control System (IICS), which successfully allows machines to see, think and do and will enable machine learning at scale. To take IICS to the next level, we’re creating an ecosystem of edge offerings to accelerate widespread adoption across the industrial sector. We’re advancing this ecosystem and empowering app developers who want to play a role in driving the new industrial era.
Currently, to add value to a software system, a developer writes the code, ports it into the legacy software stack, shuts down the devices and finally, updates it. That’s all going to change. We are working on creating an opportunity for any developer to create value-added edge applications. Customers will be able port the necessary apps to their machine without having to shut it down, just like we do on our phones today. Companies will be able to download apps for their needs and update frequently to ensure their business is running smoothly. While no one likes to run out of battery on their smart phone, an outage for a powerplant is far more costly, so the ability to port apps without shutting down devices and being able to detect issues before it occurs will be a game changer.
From wind turbines to autonomous cars, edge computing is poised to completely revolutionize our world. It’s forcing change in the way information is sent, stored and analyzed. And there’s no sign of slowing down.
The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).
IoT Market Map -- Goldman Sachs
What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!
IoT vision is not net reality
Simply stated, the Internet of Things is all about the power of connections.
Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.
Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.
Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;
- real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
- assessment of consumers, their emotions & buying sentiment,
- managing smart systems and operational processes,
- reducing operating costs & increasing efficiencies,
- predicting outcomes, and equipment failures, and
- monetization of consumer & commercial big data, etc.
IoT reality is still just a vision
No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.
Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!
Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.
A tech ecosystem is born
IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;
- low-cost hardware platforms (sensors, smart devices, etc.),
- a stable base of suppliers, developers, vendors & distribution,
- interoperability & security (standards, encryption, API's, etc.),
- local to global telecom & wireless services,
- edge to cloud networks & data centers,
- professional services firms (and self-proclaimed experts),
- global strategic partnerships,
- education and STEM initiatives, and
- broad vertical market development.
I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"
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