Eight Trends of the Internet of Things in 2018

Ahmed Banafa
January 9, 2018

 

The Internet of things (IoT) is growing rapidly and 2018 will be a fascinating year for the IoT industry. IoT technology continues to evolve at an incredibly rapid pace. Consumers and businesses alike are anticipating the next big innovation. They are all set to embrace the ground-breaking impact of the Internet of Things on our lives like ATMs that report crimes around them, forks that tell you if you are eating fast, or IP address for each organ of your body for doctors to connect and check [5].

In 2018, IoT will see tremendous growth in all directions; the following eight trends are the main developments we predict for next year (Figure 1).

Figure 1: eight trends of IoT in 2018.

 Figure 1: Eight trends of IoT in 2018.

Trend 1: Lack of Standardization Will Continue

Digitally connected devices are fast becoming an essential part of our everyday lives. Although the adoption of IoT will be large, it will most likely be slow. The primary reason for this is lack of standardization.

Though industry leaders are trying to develop specified standards and get rid of fragmentation, it will still exist. There will be no clear standards in the near future of IoT. Unless a well-respected organization like IEEE stepped-in and leads the way or the government imposes restrictions on doing business with companies if they are not using unified standards [6].The hurdles facing IoT standardization can be divided into three categories, namely:

  • platform: this part includes the form and design of the products (UI/UX), analytics tools used to deal with the massive data streaming from all products in a secure way, and scalability;
  • connectivity: this phase includes all parts of the consumer’s day and night routine, from using wearables, smart cars, smart homes, and in the big scheme, smart cities. From the business perspective, we have connectivity using IIoT (Industrial Internet of Things) where M2M communications dominating the field;
  • applications: in this category, there are three functions needed to have killer applications: control “things”, collect “data”, and analyze “data”. IoT needs killer applications to drive the business model using a unified platform.

All three categories are inter-related, you need all them to make all them work. Missing one will break that model and stall the standardization process. A lot of work needed in this process, and many companies are involved in each of one of the categories, bringing them to the table to agree on unifying standards will be a daunting task [12].

Trend 2: More Connectivity and More Devices

The speedy proliferation of IoT in past 3 years has resulted in billions of interconnected devices. As the consumer continues to stay hooked to more gadgets. The number of connected devices grew exponentially every year. By 2018 it will at least double and touch a whopping the mark of 46 Billion by 2021. More IoT devices will enter the channels, more than ever before. A clear indication of our direct dependency over the gadgets and that’s how our future is shaped [6].

As IoT continues to expand we will certainly see an increase in devices connected to the network in different areas in business and consumer markets. Smart devices will become the de-facto for people to manage IoT devices. The benefits of using smart devices in that capacity include boosting customer engagement, increasing visibility, and streamlining communication that will include new human-machine interfaces such as voice user interface (VUI) or Chatbot [4][2].

Trend 3: “New Hope” for Security - IoT & Blockchain Convergence

As with most technology, security will be the major challenge that needs to be addressed. As the world becomes increasingly high-tech, devices are easily targeted by cyber-criminals. Evans Data states that 92% of IoT developers say that security will continue to be an issue in the future. Consumers not only have to worry about smartphones, other devices such as baby monitors, cars with Wi-Fi, wearables and medical devices can be breached. Security undoubtedly is a major concern, and vulnerabilities need to be addressed.

Blockchain is a “new hope” for IoT Security. The astounding conquest of Cryptocurrency, which is built on Blockchain technology, has put the technology as the flag bearer of seamless transactions, thereby reducing costs and doing away with the need to trust a centered data source.Blockchain works by enhancing trustful engagements in a secured, accelerated and transparent pattern of transactions. The real-time data from an IoT channel can be utilized in such transactions while preserving the privacy of all parties involved [4][2].

Figure 2: advantages of blockchain.

Figure 2: Advantages of blockchain.

The big advantage of blockchain is that it's public. Everyone participating can see the blocks and the transactions stored in them. This doesn't mean everyone can see the actual contents of your transaction, however; that's protected by your private key.

A blockchain is decentralized, so there is no single authority that can approve the transactions or set specific rules to have transactions accepted. That means there's a huge amount of trust involved since all the participants in the network have to reach a consensus to accept transactions.

Most importantly, it's secure. The database can only be extended, and previous records cannot be changed (at least, there's a very high cost if someone wants to alter previous records) [11][4][5][8].In 2018 increased interest in Blockchain technology will make the convergence of Blockchain and IoT devices and services the next logical step for manufacturers and vendors, and many will compete for labels like “Blockchain Certified” (Figure 2).

Trend 4: IoT Investments Will Continue

IDC predict that spending on IoT will reach nearly $1.4 trillion in 2021. This coincides with companies continuing to invest in IoT hardware, software, services, and connectivity. Almost every industry will be affected by IoT, which means many companies will benefit from its rapid growth. The largest spending category until 2021 will be hardware especially modules and sensors, but is expected to be overtaken by the faster growing services category. Software spending will be similarly dominated by applications software including mobile apps.

IoT’s undeniable impact has and will continue to lure more startup venture capitalists towards highly innovative projects. It is one of those few markets that have the interest of the emerging as well as traditional venture capital. While the growth next year is firmly attested and the true potential is yet to be unearthed, IoT ventures will be preferred over everybody else. Many businesses have assured adding IoT to their services model from the Transportation, Retail, Insurance and Mining industries [4][6].

Trend 5: Fog Computing Will Be More Visible

Fog computing allows computing, decision-making and action-taking to happen via IoT devices and only pushes relevant data to the cloud, Cisco coined the term “Fog computing “and gave a brilliant definition for Fog Computing: “The fog extends the cloud to be closer to the things that produce and act on IoT data. These devices, called fog nodes, can be deployed anywhere with a network connection: on a factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras.”

The benefits of using Fog Computing are very attractive to IoT solution providers, some of these benefits: minimize latency, conserve network bandwidth and operate reliably with quick decisions. Collect and secure wide range of data, move data to the best place for processing with better analysis and insights of local data. Blockchain can be implemented at the level of fog nodes too [11].

Trend 6: AI & IoT Will Work Closely

Amalgamation of IoT data analytics with AI for applications ranging from elevator maintenance to smart homes, will progress rapidly over the coming two years. Platform and service providers are increasingly delivering solutions with integrated analytics designed to feed data directly into AI algorithms. Another important advantage of using AI is supporting the optimization and adaptation of both IoT devices and related processes and infrastructure.

AI can help IoT Data Analysis in the following areas: data preparation, data discovery, visualization of streaming data, time series accuracy of data, predictive and advance analytics, and real-time geospatial and location (logistical data) (Figure 3) [9].

 Figure 3: AI and IoT data analysis.

Figure 3: AI and IoT data analysis.

Trend 7: New IoT-as-a-Service (IoT-a-a-S) Business Models

Transformational business models will develop in many IoT verticals over 2018-2019, supported by Big Data and AI tools. In these models, the value is in the convenience of the service for end customers (on-demand and not requiring heavy upfront expenditure), and the usage data that is collected, analyzed, and fed back into suppliers’ businesses and processes.

But the potential for IoT business model transformation extends beyond this, to encompass an increasing variety of more complex, as-a-service business models that disrupt existing industries, particularly for areas such as heavy industry, transport and logistics, and smart cities.For these industries, IoT solutions can enable more of an ongoing, managed service relationship with both technology providers and end customers. One selling point is that costs can be more directly linked to ongoing measured usage or to specific trigger events captured by IoT sensors (e.g., “break-the-glass” solutions in which sensors pick up when a building or car is broken into). Another is that costs may be spread over time, shifting from upfront Capex to a more regular Opex outflow. Examples of such models include lighting-as-a-service (L-a-a-S), rail-as-a-service (R-a-a-S), and even elevators-as-a-service (E-a-a-S) [1].

Trend 8: The Need for Skills in IoT’s Big Data Analytics and AI Will Increase

Dynamic data sharing is at heart of IoT and Big Data Analytics will be instrumental in building responsive applications. Integrating IoT data channels with AI to retrieve on demand analytical insights has already gained momentum this year and will definitely grow exponentially in 2018. Subsequently, the need for Big Data and AI skills will rise, while most IoT service providers have highlighted the shortage for such extensively skilled candidates, internal learning programs in close proximity with R&D has set to be launched in many companies [1][10][8].

References:

  1. http://www.ioti.com/strategy/five-internet-things-trends-watch
  2. https://mobidev.biz/blog/iot-trends-for-business-2018-and-beyond
  3. https://www.bayshorenetworks.com/blog/breaking-down-idc-top-10-iot-predictions-for-2017
  4. https://readwrite.com/2017/10/03/6-iot-trends-2018/
  5. https://lightingarena.com/internet-things-anticipated-trends-2018/
  6. https://medium.com/@Unfoldlabs/seven-trends-in-iot-that-will-define-2018-2a47e763731c
  7. https://datafloq.com/read/iot-and-blockchain-challenges-and-risks/3797
  8. https://www.bbvaopenmind.com/en/five-challenges-to-iot-analytics-success/
  9. https://www.bbvaopenmind.com/en/why-iot-needs-ai/
  10. https://www.technologyreview.com/s/603298/a-secure-model-of-iot-with-blockchain/
  11. https://datafloq.com/read/fog-computing-vital-successful-internet-of-things/1166
  12. https://iot.ieee.org/newsletter/july-2016/iot-standardization-and-implementation-challenges.html

Figure credits: Ahmed Banafa


 

Ahmed BanafaAhmed Banafa has extensive experience in research, operations and management, with focus on IoT and AI. He is a reviewer and a technical contributor for the publication of several technical books. He served as a faculty several at well-known universities and colleges, including the University of California, Berkeley; California State University-East Bay; San Jose State University; and University of Massachusetts. He is the recipient of several awards, including Distinguished Tenured Staff Award of 2013, Instructor of the year for 2013, 2014, and Certificate of Honor for Instructor from the City and County of San Francisco. He was named as number one tech voice to follow by LinkedIn in 2016, his research featured in many reputable sites and magazines including Forbes, IEEE and MIT Technology Review.

 

 

Comments

2018-03-13 @ 10:30 PM by Bokka, Sharmila

Loved your article... But what is the difference between M2M communication and IOT. I always assumed IOT and M2M kinda like synonyms because both mean connectivity.

IoT Trends in 2018: AI, Blockchain, and the Edge

Bret Greenstein
January 9, 2018

 

The Internet of Things has come a long way since the early 80s, when a group of Carnegie Mellon students turned a Coke machine into what may be the first Internet appliance[1]. Their journey began after these students became growing frustrated to find out the machine was empty. That lone connected device has since been joined by billions of others – an estimated 29 billion within the next two years, according to IDC – spanning industries as diverse as automotive, manufacturing, electronics, aerospace, and almost any other industry you can imagine.

Now, as we embark on a new year, the Internet of Things (IoT) is continuing to evolve away from just connected devices, and converging with new technologies like AI and blockchain to help us be more productive, better informed, and to live with new levels of convenience. Further, with the application of edge computing and IoT in industrial solutions, 2018 will be a revolutionary year in improving human’s ability in the workforce.

AI is the Future

Augmented intelligence (AI) is helping to usher growth and prosperity in every industry across the globe. This intelligent technology is developed and used to “augment” performance and improve outcomes, but does not replace human power. It’s man and machine, not man versus machine – an important distinction when it comes to applying AI to IoT.

The convergence of AI and IoT means that these physical devices can now see, hear and understand the world around them. They can make sense of the vast amount of unstructured data that is being produced and then provide businesses with more intelligent insights that enable more innovative uses which will directly benefit all of us - both professionally at work, and personally at home.

For example, IoT is now being used in places like hospitals, hotels and conference rooms and the interactions with these locations are as seamless as ever. Now, you can simply speak to these connected rooms to adjust the blinds or ask for more information about your physician, or even the weather. These types of activities can be applied throughout our day to day functions: from smart alarm clocks that signal coffee makers to automatically brew in the morning, to cars being able to send an alert about black ice ahead and suggest an alternative route, to mobile devices that can identify when you’re running late to work and suggest an email to notify colleagues.

This new era, an era of man + machine, will bring new levels of innovation, efficiencies and convenience to our hyper connected world.

Industry 4.0

Within two years, IoT will be the single greatest source of data on the planet. This data will be generated by billions of interconnected sensors and devices that are embedded into the world’s physical systems. These connected devices and their data are the heartbeat of the fourth industrial revolution (Industry 4.0), with the promise of significantly improving more than a business’s bottom line, but ensuring employee safety, product quality, processes, and time to market.

In 2018, we will continue see massive growth in the use of IoT in industrial and manufacturing industries, with improved automation and self-configuration, cognition and intelligent support to significantly improve human abilities in the workforce.

This “Smart manufacturing” will increase efficiency, productivity, safety as well as product output. In addition, industrial and manufacturing organizations will achieve quicker production rates and speed to market, with increased accuracy and data available using IoT.

Pushing Computing to the Edge

When we think about how IoT is expanding to new industries, edge infrastructure is a critical component that is making this all possible. According to IDC, IT spend on edge infrastructure will reach up to 18 percent of total IoT infrastructure spend by 2020. There’s two components of edge that will be prevalent this year.

First, businesses with oil rigs, factories, shipping operations and mines where the bandwidth is poor, will leverage edge devices and apps to extend their cloud functions and as these connected devices become smaller and more inexpensive, not only will their deployment extend, their capabilities will as well.

The second component of edge computing to watch in 2018 will be how cognitive capabilities are increasingly being pushed to the edge, such as the ability for cameras to see images and listen to sounds, interpret them, and then perform an action. These types of applications and voice analytics will be a huge source of growth within IoT throughout the year, and they will contribute to more mainstream consumer use, like a car’s navigation system that can switch from the cloud to the edge and back when driving through tunnels or any other hard to reach places.

This extension of cloud – and cognitive – capabilities on the edge will increase the strength and accessibility of the IoT network, and deliver actionable and smarter insights for maximum impact.

Blockchain Upping IoT Transparency

Blockchain will transform the transparency and assurance of transactions, and when you marry this with IoT, it enables IoT devices to share critical data across businesses and across processes.  This will help businesses across all industries to have a powerful tool to transform their business and ecosystem.

It is critical that sensitive information collected by connected devices is kept safe, especially as the amount of data continues to grow. Working on a blockchain requires every party involved to verify each transaction, enabling businesses to track IoT data as it moves from device to device – preventing disputes, upholding accountability, and maintaining secure, transparent and accurate transactions. Applying blockchain to IoT also creates more scalable and cost-effective solutions by creating a secure solution that does not require central control and management.

For example, with an IoT-enabled blockchain, freight companies can store temperatures, position, arrival times, and find out the status of shipping containers in order to speed up delivery times. Or manufacturers that create components on vehicles and aircrafts can provide greater insight and visibility in safety and regulatory compliance. While we are still in the early stages of combining blockchain and IoT, this will be an exciting area to watch as companies determine how to apply them to their business together and demonstrate the real potential that they hold.

After a decade of working with more than 6,000 IoT adopters spanning the automotive, oil and gas, transportation, aerospace and defense industries, I am excited at the prospect of IoT, especially as more businesses understand the true value IoT brings to their bottom line. The sky is the limit. Just consider this fact--IBM predicts that the data derived from these connected devices will produce insights that drive economic value of more than $11 trillion by 2025. I am sure that 2018 will see truly transformative partnerships and innovations that will create new possibilities for businesses and industries.

[1] http://www.cs.cmu.edu/~coke/


 

Bret GreensteinBret Greenstein is Vice President of IBM Watson’s Internet of Things Consumer Business, aimed at reaching consumers at scale by establishing new IoT products and volume offerings by tightly collaborating across IBM business units and leveraging capabilities from The Weather Company. Since joining IBM Watson IoT, Bret and his team have been instrumental in helping industrial clients around the world to design, build, and operate connected things through software solutions and industry expertise. Bret has over 28 years of technology and leadership experience in all aspects of IBM's business. He holds patents in the areas of collaboration systems and is a graduate of Rensselaer Polytechnic Institute with a Bachelor degree in Electrical Engineering, and a Master’s degree in Manufacturing Systems Engineering.

 

 

Comments

2018-01-12 @ 2:25 AM by Shekhawat, Rajveer

Hi Bret! Good exposition on what's coming up in IoT domain. But as you are aware, there is major segment of IoT devices running on battery and block chains in its present form would not suit due to its heavy power consumption requirements. We need to explore so.e light weight security and trust mechanisms.

 

Smart City Application Enablement Platform

Kim Khoa Nguyen
January 9, 2018

 

Today, cities are largely considered as innovation drivers in areas such as health, environment, technology and business. The emerging concept of “smart cities” has quickly evolved beyond superficial use of the term for the purpose of pure city marketing, but shed more light in particular on the defining role of the digitally networked society and user-driven innovation.

A new holistic definition of a “smart” city is when “investments in human and social capital and traditional and modern Information and Communications Technologies (ICT)-based infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory government”[1].

Smart city enablement platform

In such future smart cities, an application enablement platform (SCAEP) is required to integrate Internet of Things (IoT), computing, and networking technologies to empower individuals, organizations, and society to realize the benefits latent in systems and data at scale. SCAEP enables real-time situational awareness in the urban system of systems by its ability to gather and integrate data at scale, securely and privately, from environmental, critical infrastructure, health and personal sensors. Analytics and learning services from the Business Intelligence layer when applied to historical and real-time data lead to better decision-making, and through open APIs, enable a vast array of smart applications that can be tailored to the specific needs and priorities of an urban community - economic, health, creative, cultural, governance, mobility, energy, safety, environmental. Virtualization of all resources (sensors, computing, programmable hardware, wireless/optical communications, GPUs, networking) and a software-defined approach to services allows SCAEP to ride advances in these enabling technologies, and to keep pace with increasing data volume/velocity, validity/veracity, and network/system diversity and complexity. To some extent, SCAEP is an eco-friendly platform that federates and interconnects a cluster of cities within a country and internationally.

Supporting IoT applications at scale in smart city

Today, IoT encompasses sensor/actuator devices that are connected to intelligent systems to create smart applications that assist decision-making and control autonomous behavior. IoT applications have attracted intense commercial interest, especially in industrial applications. IoT also plays a crucial role in smart cities. However, the first IoT systems address vertical industries that use incompatible protocols in IoT islands, falling short of the ultimate vision of a ubiquitous IoT. The potential for sensor networks to support multiple applications was recognized early on, and initial approaches used Service-Oriented Architecture approaches to enable service composition. Recent approaches use cloud computing to enable a broad range of IoT applications, and some proposals advocate edge computing to support IoT applications with real-time requirements.

Existing sensor technologies (loop detectors in roads, surveillance cameras in buildings and highways, crowdsourced tracking apps, smart meters) already provide hundreds of thousands of data streams in major cities. However, low-cost sensors, actuators, and wireless networks in IoT will lead to an explosion in the volume of sensor data in smart cities. Realizing the benefits of the smart city vision therefore requires dealing with the joint challenges of widely distributed data, connecting it with computing resources to extract intelligence and make decisions, and dispatching commands to take actions, all at scale.

The huge number of “things” in IoT implies that IPv6 is needed for devices that are directly connected to the Internet. However, “things” exist in highly dynamic, context-dependent, mobile, heterogeneous environments that IP has difficulty handling. ICN (Information-Centric Network) is preferred for data dissemination in IoT. ICN is a class of Future Internet protocols where packets are routed based on names rather than addresses. ICN addresses a central concern of IoT by allowing the network to work directly with named data and services. Mobility First (MF) and Named Data Networking (NDN) approaches that combine unique identifiers with dynamic ID-to-network address resolution are promising for smart city IoT.

Software-defined networking (SDN) is transforming networking. By separating the control plane from the data plane SDN allows an external controller to define the treatment of data flows in streamlined commodity switches. This leads to greater flexibility in the network services that can be provided and gives the operator much greater control over the flows in its network. SDN has been proposed to meet the needs of ICN. Virtualization of IoT with a view towards SDN has also been investigated recently. Intel’s OneM2M reference architecture for IoT infrastructure that includes sensor/actuators, gateways, fog computing, network, and cloud/datacenter has foreseen addition of containers, virtual machines (VMs) and SDN. In 2016, USIgnite and NIST introduced a SmartCity Challenge to bring together cities and researchers to tackle the following challenges in creating smart cities and sharing applications: Accommodating the scale of IoT; Achieving ultra-low latency; Leveraging SDN for resource slicing and data isolation; Enabling gigabit community nodes; Providing one-hop interconnection among testbed cities; Delivering responsiveness and availability to support cyber-physical systems; and low-latency applications using 5G and LTE networks. To realize smart cities, it is not enough to address these challenges separately; but a coordinated approach to these challenges by designing and deploying a SCAEP.

SCAEP use-case

The Open-Air Laboratory for Smart Living[2] is a testbed created by Videotron, a major Telco carrier in Canada, in 2016 in collaboration with Ericsson, the École de technologie supérieure (ETS) and Montréal’s Quartier de l’Innovation, to offer researchers, companies and residents a unique environment and the infrastructure for on-the-ground, real-world tests of technologies aiming to improve and simplify the daily life of Canadian (www.labvi.ca). As the first smart city model that leverages on 5G technology in Canada, the testbed is operated and orchestrated by a SCAEP co-developed by Ericsson and ETS (Figure 1). It supports IoT smart city applications coming from more than 20 companies, many of them are spin-offs and start-ups, classified into three categories. The first category regroups infrastructure providers, like gigabit access, LoRa (Low-Power radio) access, WiFi SON (Self-Organizing Network) access, visible light communication (LiFi) access, and public sensing access. The second category contains platform providers that rely on the infrastructure provided by the fist category to develop platforms for data collection and processing, like data-centric security platform, data analytic platform, image processing platform. The third category includes data exploring service providers that use data coming from the second category to develop their business intelligence, like air quality alarming service, emergency service, living experience service, utility management service, virtual event organizing services, and virtual economy service.

Figure 1: Open-Air Lab testbed.

Figure 1: Open-Air Lab testbed.

[1] https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/public-sector/deloitte-nl-ps-smart-cities-report.pdf

[2] http://www.labvi.ca


 

Kim Khoa NguyenKim Khoa Nguyen is Associate Professor at the Department of Electrical Engineering at the University of Quebec’s Ecole de technologie supérieure (ETS), Montreal, Canada. He received his Ph.D. degree from Concordia University. He served as CTO of Inocybe Technologies, and was the architect of the Canarie’s GreenStar Network and also involved in publishing CSA/IEEE standards for green ICT. He has worked for Alcatel Systems (now Nokia) and a nationwide operator in Asia-Pacific. His expertise includes smart city, cloud computing, IoT, big data, data center, network optimization, high speed networks, and green ICT.

 

 

Livin’ on the Edge

Rebecca Haass
January 9, 2018

 

Edge computing seems to be what everyone in IoT is talking about these days. But what exactly is it and why is it so important?

Currently in most big data solutions, information is collected from a sensor, sent to the cloud, processed in the cloud and then sent back to the device the sensor is on. For example, let’s say I own 10 parking lots across the Midwest. I’ve installed sensors on each of the overhead lights that illuminate the lots. The sensors will tell the lights when to turn on or turn off based on how much sunlight is available. So, on a rainy day with little sunlight, the sensors will make sure the lights are on in the parking lot until the rain passes and the sunlight illuminates the lot. However, the information from the sensors is not being processed on site, but rather on the cloud. This can lead to a delay in when the lights turn off or on.  This is when edge computing comes into play. Edge computing is done on site or at the “edge” of a device - so in this example, there could be a small processing unit that is running the mechanism that is turning on and off the light on site. This allows the actions to be taken in real-time rather than having a delay occur before the action takes place.

Edge computing is also very powerful in situations where it’s difficult to connect to the cloud. For example, in agriculture. Most farms are in rural areas where wi-fi may not be readily available. So even though tractors or sensors in the ground are collecting information, decisions such as when to turn on a water system or when an area needs to be fertilized may be delayed until data can be uploaded to the cloud to be processed. If a small processer was located on the farm and connected to the sensors via bluetooth, then these insights could be generated much faster. By having processing on the edge in this situation, data turns instantaneously into action.

Also, in situations where enormous amounts of data are generated every second. For example, on planes, sensors are connected to almost every part – they’re on the wings, the engine, the landing gear, etc. According to Forbes[1], today on an average flight between 60-100 gigabytes of data is collected and over the next ten years this number is expected to increase to between 5-8 terabytes of data generated on each flight. Could you imagine the costs of uploading this information in real-time to the cloud while the flight was in the air? In situations like this, its integral to have a processor located on the device (or plane, in this case) to make in the moment decisions without having to worry about the additional costs of sending all of this data to the cloud to be processed.

Edge computing has become more common place with the adoption of small inexpensive processors, such as the Raspberry Pi. Raspberry Pis and similar devices allow companies to place processors on devices and make decisions in the moment. In the future, devices like these will power the majority of IoT solutions. However, we shouldn’t expect the cloud to be abandoned. Cloud computing will still play a major role when it comes to storing data and making prescriptive decisions based on that data.

However, for most decisions that need to happen in the moment we’ll need to go through the cloud and onto the edge!

[1] https://www.forbes.com/sites/oliverwyman/2017/06/16/the-data-science-revolution-transforming-aviation/#1548b4ff7f6c


 

Rebecca HaassRebecca Haass has over 10 years of experience in the big data field and is currently working as the Marketing Director at Entrigna, an IoT and Big Data solutions provider. Rebecca started her career as a database administrator who supported both the CRM and ERP systems at Abbott Spine. It was during this time that she discovered how big data can be used to help businesses increase market share and also provide valuable services to customers. Rebecca has also had roles at GE Healthcare, Zimmer Spine and Eli Lilly. Rebecca enjoys writing about how technology can improve people’s lives and all things Notre Dame.  Rebecca has a BS in Computer Science, a BA in Theology and an MBA, all from the University of Notre Dame.