DZone Research: IoT Success Factors

To understand the current and future state of IoT, we spoke to more than a dozen IT executives active in the space. Here's what they told us when we asked, "What are the keys to a successful IoT strategy?":

Security

• Security: IoT devices are expensive, and failure or security-vulnerabilities could have a real-life impact on their users or the people around them.You need to ensure that you’re protecting your deployment itself and the data flowing through it. There are many security strategies, but important ones include end-to-end encryption, access management, secure firmware/provisioning, and open-port management.
• Reliability: You need reliability at scale, not just in the lab. You need a persistent and high-speed connection that can handle any amount of data, and an uptime guarantee to maintain service to your deployment. Your infrastructure should be globally scaled, so as users are added further away from your points of presence, performance doesn’t falter.
• Low-latency: Speed is key. You should use the low-latency protocols and technologies to provide the real-time speed necessary to control and monitor IoT devices. Every millisecond counts.

Scalability

• 1) Do they have a requirement to scale beyond their first requirement? Determine the platform size needed. Will edge devices have to process on a quarterly or annual basis? The platform has to scale for years. 2) Security – transfer data to the data center. We need to focus on edge processing as IoT is becoming self-intelligent. 3) Capacity with scale and access and process data at the edge – how to maintain and how long? Trying to marry an enterprise benefit in a condensed form. Edge node and solutions come into use. Autonomous cars TBs on an hourly per car basis. The car becomes the edge node. The first level of data classification to the edge node leads to edge cluster use cases. Small form factors of nodes in edge environments. Offer three or five node clusters that can scale. Customers who fall into self-contained intel devices. Second are people beyond IoT to edge node and edge clusters want enterprise in a small briefcase.
• Lightweight and secured — IoT devices tend to be small in form-factor (i.e. low power requirement, limited RAM, disk space, etc.). So, it’s important to pay great attention to resource consumption/requirement. And due to the fact that IoT is all about scaled-(smart) devices-deployed, scalability and security of these devices are the main focus.
• One key is planning for the quantity of data which will be created. We have worked with many companies that started pilot IoT projects and quickly realized that the architecture they had created would not scale to support their application. Building an IoT platform on a massively scalable, highly reliable, high-performance platform from the start can avoid speed and scalability challenges that arise with production-scale deployments.

Integration

• Make no mistake — adoption of IoT technologies is mandatory for organizations to stay ahead of their competitors and continue meeting the demands of their customers. Organizations, therefore, need to improve their ability to adopt these technologies without introducing unacceptable risk. An application network approach offers the ideal solution, allowing organizations to deploy IoT assets as modular pieces that are introduced incrementally, visibly and intentionally. These assets are exposed as APIs, which can be configured under a ‘zero-trust’ model, creating layered defenses that prevent the API from trusting new entrants prematurely. As organizations continue to add new technologies at pace, this approach is vital to allow them to simultaneously minimize and mitigate the associated risks.
• The past two years have seen an explosion of platforms as Amazon and Google Home has become more pervasive. We need integration. Ecosystems are still in the early stages for the consumer – a fully autonomous smart home. We need to look for other ways to delight the customer so they see the value in these devices.
• Get all parties together. Avoid functional isolation. Involve data science from the beginning. Determine what you are trying to accomplish.
• Standards, code, and certifications. Specifications and certification are key. Also having the open source project to provide a reference implementation of the OCF spec. Other keys are focused on interoperability and security throughout the IoT pipeline.

Other

• Bluetooth low energy is a common protocol. Do what you do best. Identify a hardware partner you are comfortable working with. The device comes alive on the software size. UX and software make the difference. We work with 3M to create a smart air filter with a sensor to determine the usable life remaining. Hardware, cloud backend, an app on iOS and Android, all three work together to proactively remind to change, use Amazon to order replacements just-in-time.
• We work with manufacturing, energy, healthcare, and retail. Manufacturing and energy know what they want to do with heavy machinery generating data. They have an existing connected asset within the business and they want to make sure to monitor those assets so they can move to predictive maintenance. We coach them with regards to 1) data problems – data plane, 2) deploying and managing new infrastructure, 3) providing applications and services, 4) modernizing and improving streams of data or cloud-native applications, and 5) how to deploy across hybrid environments.
• The fundamentals are not that different than cloud software development. Need a good software development process for testing, working with a team, deploying across a lot of systems. Understand the system you are developing on. Key to adoption is to create industry-specific solutions.
• Keep your feet on the ground even if your head is sometimes in the clouds (or cloud). To put it another way, while it’s necessary to keep up with the latest technologies, trends, and offerings in the IoT space, keeping the actual needs of your customer in mind is critical. Be the bridge between cutting-edge technologies and what our customers truly need right now. Otherwise, you can fall into the trap of build a cool technology solution without addressing or fixing an existing problem.

Here’s who we spoke to:

• Mike Donovan, V.P. of Product, Aquicore
• Adam Fingerman, CEO, ArcTouch
• Dave Schuman, Mobility Leader, Cloudera
• OJ Ngo, CTO and Co-founder, DH2i
• Nikita Ivanov, Founder and CTO, GridGain Systems
• Suzy Visvanathan, Director of Product Management, MapR
• Uri Sarid, CTO, MuleSoft
• David McCall, President, and Clarke Stevens, Chair, Data Model Tools Task Group and Vice Chair, Data Modeling Work Group, Open Connectivity Foundation
• Zach Supalla, Founder and CEO, Particle
• Stephen Blum, CTO, PubNub
• David Bericat, Global Technical Lead, Industrial IoT and Edge Computing, Red Hat
• Vaughn Shinall, Head of Product Outreach, Temboo
• Ray Wu, CEO, Wynd