MOVING BEYOND PROOF OF CONCEPT (PoC) IN IoT

WHITEPAPERInternet of Things

MOVING BEYOND PROOF OF CONCEPT (PoC)IN IoTYears in the making, Internet of Things (IoT) is finally on the cusp of taking off entirely; disrupting businesses, gov-ernments, and consumers and the ways they interact and exchange information. The opportunities generated by IoT are expected to open doors to unique customer experiences, business models, revenue streams, and opera-tional efficiencies. Enterprises are investing in IoT technology through concentrated efforts in R&D, IT, Marketing, and Operations. Zinnov estimates a spend of $ 167 Billion spend on IoT technology products and services in 2017. The spend is expected to increase by ~20% CAGR to reach $ 410 Billion by 2022.

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OVERVIEW

Advances in the enabling technologies are accelerating IoT adoption. Declining sensor and computing cost along with shrinking form factor is allowing chips to be embedded in everyday devices. Modern device communication technologies such as LoRa, Sigfox, NB-IoT, Zigbee, BLE are contributing towards a connected world. Artificial Intelligence and new-age interfaces such as AR / VR are further helping in the proliferation of IoT adoption.

However, full-scale IoT adoption has belied expectations, with markets readjusting the expectation of the number of connected devices from 50 billion to 26 billion by 2020. While enterprises are investing in exploring IoT, most have not been able to scale up their implementations.

According to a Cisco survey of 1,845 business IoT leaders, 60% of IoT initiatives stall at the Proof of Concept stage. Out of the remaining 40% IoT projects, only 26% were considered successful. Lack of clear business objectives and use cases is proving to be the bane of IoT deployment across enterprises. The increasing complexity, as enterprises move from ‘Exploration’ to ‘Proof of Concept’ to ‘Large Scale implementation’ is a challenge impeding wide-scaleIoT adoption.

This whitepaper will analyze the current challenges in large-scale IoT implementation, and the steps to overcome these challenges.

https://newsroom.cisco.com/press-release-content?articleId=1847422


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IoT ARCHITECTURE COMPLEXITY IS THE ROOT OF MOST CHALLENGES

The complexity of IoT starts with its architecture, which comprises of the infrastructure layer (including end-points and communication layer), the platform layer, and the application layer, that enables enterprises to drive expected business outcomes.

BUSINESS OUTCOMES

IoTAPPLICATIONS

IoTPLATFORMS

IoTINFRASTRUCTURE

Device Abstraction and Management, Edge-to-cloud Connectivity,CEP & Workflow Management, Security

Communications (Gateways, Connectivity Protocols, Networks)

Sensors; SCADA

Software Defined Infrastructure

Storage:Cloud Infrastructure,

Data Lakes

Compute:High Performance

Compute

Security(MDM,

End Point encryption)

Customer Experience

Visibility Intelligence

Operational Efficiency New Products & Services

Inventory Management

Production Optimization

Logistics Optimization

Operations Risk &Intelligence

As-a-service Model

Data Monetization

Platformization

Figure 1: Internet of Things (IoT) architecture

Control Autonomy


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THE COMPLEXITY OF THE IoT ARCHITECTURE SPAWNS THE FOLLOWING CHALLENGES FOR ENTERPRISES:

Lack of In-House ExpertiseEnterprises generally lack the in-house talent and expertise to work on new technologies and deduce outcomes at the desired speed and scale. Usually, multiple technology vendors provide expertise in specific layers of the IoT stack, very few vendors, if at all, offer end-to-end solutions. Hence, vendor selection and integration among the different layers is a key challenge. In addition, enterprises also lack the ability to keep up with the rapidly evolving IoT technology landscape.

Security and PrivacyThe recent hacking of baby monitors, smart refrigerators, cameras presage the security nightmares that could be caused by IoT devices. IoT devices have resulted in the rise of Distributed Denial of Service (DDoS) attacks as hackers can easily get into the off-the-shelf hardware that presents weak security measures. IoT gadgets work on two-way communication of information - with operational data being sent out of the device, and operating instructions and updates being received by the device. These smart devices can be tweaked to gather and leak personal and sensitive information about users. In-cloud security, as well as the security of data going to the cloud, continues to be the biggest challenges associated with scaling IoT solutions.

Regulatory A survey by Gemalto3 indicates that a majority of IT and business decision makers prefer an increase in government regulations to protect data across the IoT ecosystem. Also, most governments have not taken a clear stand on the rules and regulations related to IoT. Due to the varying connectivity, cellular telephony standards, and regulatory requirements across countries, global rollouts of an IoT solution has become a significant challenge.

Interoperability / StandardizationTechnology companies have been developing solutions independent of each other, using proprietary platforms and frameworks. Most endpoints run on different application-level protocols and profiles, making it difficult to integrate them. Though there has been a market movement towards standardization with big enterprises backing alliances and consortiums such as ‘Industrial Internet,’ ‘Zigbee Alliance,’ ‘Thread,’ and so on, the presence of multiple such alliances defeats the purpose. The challenge will exist until proprietary software moves toward open source and alliances merge, as seen when Allseen Alliance merged with Open Connectivity Foundation. Large-scale implementation can be better achieved when the interoperability and standardization challenges are better addressed.

http://www.govtech.com/blogs/lohrmann-on-cybersecurity/lack-of-trust-in-iot-security-means-more-regulation-is-coming.html


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Data storage, Management, and AnalyticsIoT is expected to generate 44 Zettabytes of data by 2020. This unprecedented colossal volume of data requires a new approach to data management. Decisions such as which data points to collect from which sensor, at what frequency, and what data to store vs. what to discard are difficult for enterprises. Additionally, the IoT devices produce unstructured data which outdates traditional relational data management methods. Decisions about storing on-premise or on public/private cloud and their cost to risk assessment are added pain points. Furthermore, incorrect or inaccurate analysis of collected data points may lead to a complete failure of IoT programs.

ROI QuantificationThe initial IoT frenzy resulted in a gold rush with technology companies and service vendors jumping in to grab a share of the exciting new opportunity. A number of enterprises entered the fray with trials around IoT, some undertaking it just because their peers had started exploring IoT and they didn’t want to lagbehind. However, many organizations still do not know how IoT will add value to their business. There is a lack of clear definition of IoT success, and its associated roadmap of milestones, constraints, and solutions. Owing to uncertainty of returns, most enterprises are hesitant to go beyond PoC.

Upfront InvestmentIoT implementation usually requires a considerable capital expenditure by enterprises. The substantial upfront investment is making organizations reluctant to move beyond PoC and implement IoT on a large scale. Though platform and cloud providers are offering their proprietary solutions as-a-service in an opex model, models such as IoT-as-a-service where technology/service vendors provide a comprehensive solution from hardware and software to data management and data analysis bundled in a service model, are absent.

Energy Requirement / Battery lifeUnlike many larger systems, IoT devices often do not have access to a primary power supply, and yet they must ensure data collection, analysis, and transfer, round the clock. Meanwhile, users also do not wish to spend their time and effort to frequently replace batteries, especially for devices at remote locations, such as at oil rigs. Work on maximizing the life of small, onboard batteries has been ongoing, but it is still far from a comprehensive solution that enterprises expect for large-scale implementation.

Misalignment of Internal TeamsThe gap in the perception and understanding between technology executives (IT or R&D) and business executives and their lack of collaboration is another big challenge for successful large-scale IoT implementation.

https://businessintelligence.com/bi-insights/data-set-grow-10-fold-2020-internet-things-takes/


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According to Zinnov’s analysis of the quantum of IoT challenges vs. the barriers to overcome them, security and privacy, interoperability, ROI quantification, and data storage and Management emerge as the top challenges for an enterprise.

CHALLENGES FOR LARGE-SCALE IoT IMPLEMENTATIONH

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Quantum of ChallengeComplexity, Criticality, Business Impact

Lack of In-House Expertise

Energy Requirement / Battery Life

UpfrontInvestments

Misalignment ofInternal Teams

ROI Quantification

Interoperability /Standardization

Security and Privacy

Data Storage, Management and Analytics

Regulatory

Figure 2: : Analysis of IoT Challenges


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Security and PrivacyThere is a need for enterprises to establish a complete chain of trust across IoT device endpoints, network, application, and data. While building the chain of trust, enterprises should emphasize the following:

• Device Selection and Management Retaining control over the IoT network starts with the right infrastructure. Enterprises need to select the right devices that have the required security features or are white boxes, to learn how they work, to identify security gaps and add additional security features. Governments are now focusing on device selection to mitigate IoT security challenges. For instance, the US government recently proposed the ‘IoT Cybersecurity Improvement Act of 2017,’ which requires vendors of connected devices purchased by the government to make sure that the devices can be patched; that the devices do not use hard-coded (unchangeable) passwords and are free from known vulnerabilities when sold. Enterprises need to ensure similar measures to overcome device security challenges.

• Device Identity and Data Authentication Enterprises should ensure that the system authenticates any device, and vice versa, before communicating, fetching, and processing the data. There should be multiple points of authentication – at endpoints, gateways, cloud, and applications. It is essential to know which devices are part of the network and which particular device is communicating with which device. Identity management thus becomes core in a scaled-up implementation.

• End-to-End Encryption All data at rest and in transit, from the bottom layer of a device to the top layer of an application, needs to be encrypted to ensure that there is no tampering of sensitive data. ‘Device Selection’ is vital here, as the diversity of the IoT devices limits the availability to have a standard encryption process.

APPLY UPFRONT EFFORT TO OVERCOME CHALLENGES

While the challenges are critical and may require ongoing efforts to overcome, enterprises could take a few steps upfront to mitigate them. Take a look at the top four challenges for large-scale IoT implementation and the steps for effective mitigation.


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• API Security Since multiple systems consume IoT data, an IoT system requires various APIs and related security measures. API security provides security of data across devices, the back-end, and applications to ensure that only authorized devices communicate with multiple APIs.

• Vulnerability Analysis Enterprises must utilize tools for IoT vulnerability analysis for potential security threats across the stack.

InteroperabilityWhile the idea of having unified hardware, software, and communication frameworks seem ideal, the diversity of endpoints and solutions in the market makes interoperability a big challenge. To enable interoperability, enterprises should focus on the following:

• Domain-Specific Endpoint Selection In case of greenfield implementations, enterprises should look for domain-specific endpoints which run the same field protocols as their existing infrastructure and end points. This will ensure seamless integration with the existing systems as it could be. For brownfield implementation solutions, supporting multiple protocols is a necessity, as detailed in next step..

• Multi-Band / Multi-Mode Radio Units Just as mobile phones have options of running on 2G/3G/4G networks, multi-band / multi-mode devices have the option of communicating via multiple technologies such as BLE, Zigbee, and Z-wave; running a variety of profiles of application-level protocols. There is a need for aggregator devices or gateways for integrating multiple protocols and technologies to provide ease of interoperation to the user. U-blox, for instance, recently announced the world’s smallest multi-mode configurable chipset for IoT devices offering both LTE cat M1 and Cat NB15. Enterprises looking to scale but fearing potential interoperability issues should utilize multi-mode devices that cater to the most popular protocols. This could be a strategy to ensure that ongoing investment in new technology is limited and the solutions can work even as technology evolves for a considerable amount of time, ensuring ROI on initial technology investment.

• Cloud Choice Most of the cloud providers, such as AWS, IBM, Azure are trying to standardize the protocols used for communication with devices. MQTT, for instance, is among the preferred protocols by the top players. Enterprises should choose the right cloud that supports standard protocols to ensure seamless integration or migration to the cloud in the future.

http://www.u-blox.com/de/press-release/u%E2%80%91blox-announces-world%E2%80%99s-smallest-lte-cat-m1nb1-multimode-module-global-support-iot


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Return on Investment QuantificationIn traditional solutions such as ERP, there are standard benchmarks and well-established practices for calculating ROI. However, since IoT is still an emerging and evolving market with new and different use cases coming up almost every day, the standards for ROI quantification are still not streamlined. In order to calculate ROI better, enterprises should consider the following:,

• Choose the Right Business Case IoT ROI quantification starts with choosing the right business case. It is essential to identify the specific business objective (revenue addition, cost reduction, and enhancing customer experience) and area (customer relations, supply chain, operations, and more) to be addressed by the IoT to estimate cost and benefits.

• Define the Success Metrics The next step is to define the success metrics and KPIs. A metric or KPI could represent parameters such as reduced time-to-market, improved productivity, higher profitability, or related results that provide monetary gains. Stakeholders’ buy-in is essential at this stage to ensure common goals. Also, a 360-degree view of the potential constraints is required to scale up implementation.

• Factor in New Opportunities for ROI Other than just calculating the necessary cost and savings, IoT involves calculations around new opportunities for additional revenue generating capabilities, customer behavior understanding, improved customer satisfaction, better worker safety, among others. Such opportunities will help explore innovative business models, as-a-service for instance, to monetize IoT. Enterprises need to understand use cases thoroughly and analyze the potential opportunities IoT will bring in, to quantify the ROI.

• Spend Enough Upfront Time Spending upfront time is critical for IoT implementations. Enterprises must shift left, allocate extra time and effort during Exploration and PoC stages for a successful IoT implementation.


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While most organizations try to minimize Exploration and PoC costs, they tend to be far from the real large-scale prototyping. In most cases, implementation risks, costs, and timeline constraints are realized only after PoC, leading to complete scrapping of the project. Successful implementation starts with a large-scale replication in mind right from the PoC stage. Though this is bound to increase the cost of PoC, it gives a clearer picture of feasibility and minimizes any possible associated technology, business, and operational risks. The shift left of time and effort during PoC reduces the chances of projects stalling, allowing enterprises to move beyond PoC and monetize IoT.

Data Management and Analysis

While IoT is likely to increase the amount of data, not all the data is useful, and the lack of expertise to filter and interpret the useful data is a big challenge. To overcome such challenges, enterprises should focus on the following:

• Relevant Data Collection and Standardization It is crucial to understand what data needs to be collected from which sensor and at what frequency. At the same time, they need to ensure a minimum amount of data collection and storage. Also, standardization needs to be ensured in the way devices communicate and transfer data. Protocols such as MQTT, CoAP, XMPP, REST, AMQP, and DDS, though still evolving, are becoming standards which should be adopted, basis performance needs and associated scale-up risks, for easier exchange of data with IoT devices.

Figure 3: : Typical vs Ideal IoT implementation in stages

Typical IoT Implementation Ideal IoT Implementation

BusinessRisk

BusinessRisk

CostCost

Exploration ExplorationProof of Concept Proof of ConceptLarge ScaleImplementation

Large ScaleImplementation


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• Data Storage IoT data comprises of both structured data such as records and unstructured data such as images. Since conventional data management solutions may be ineffective, it is imperative for enterprises to select tools that handle both kinds of data. Depending on the requirements for throughput, latency, volume, and short / long-term storage, choosing the appropriate data storage for IoT data can help organizations overcome this data management challenge.

• Edge Analytics Data collected by the devices need to be processed for enterprises to derive their expected outcomes. Historically, the processing was done at the cloud level. However, the cost of data transfer to the cloud is a significant expense of running IoT systems. Enterprises should adopt edge and fog computing where a basic level of computing is done at the device itself or within the network, and only filtered data is transferred to the cloud for further intensive compute.

• Big Data analytics To derive insights out of the colossal volumes of data, enterprises need to apply big data analytics, which includes both batch analytics and real-time analytics. While batch analytics are typically required for vast volumes of relatively historic data, real-time analytics are required for instant feedback-based, streaming data. Additionally, deep domain expertise along with data sciences and statistical expertise is needed for understanding patterns in the data.


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A 5-STEP APPROACH FOR A SUCCESSFUL LARGE SCALE IMPLEMENTATION

Successful IoT implementation is not just about a technology change – it is about strategy, goals, culture, skills, collaboration, and leadership mandate changes. This 5-step approach will help enterprises reach their large scale IoT implementation goals:

Strategize

Define a Business Case Outline a clear business issue and build a case around it. The problem statement should help analyze the broader objectives of implementation.

Get Stakeholder Buy-InAlignment of internal and external teams will enable clear requirement analysis and detailed objectives.

Describe SuccessIt is paramount to define the success metrics, KPIs, risks, and impact on business for a large scale IoT implementation.

Focus on Scale-UpThe focus of the strategy and associated metrics should be on a scaled implementation and not on PoC. This will help analyze the overall cost, risks, and timelines, and help decide whether a PoC is feasible in the first place.

Revisit ObjectivesSince IoT implementation could be a moving target, the teams should revisit and tweak their objectives at regular intervals to analyze and cater to the constraints and conflicts that arise during the course.

Strategize Identify Choose ManageImplement& Interpret

1 2 3 4 5


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Identify

Evaluate IoT Readiness Use frameworks to assess IoT maturity and readiness of the enterprise.

Target Business LinesNarrow down the business lines, products or operations that require transformation.

Identify OwnersKey business and technology stakeholders who will own IoT implementation must be identified.

Create RoadmapDetail out a clear roadmap, exhaustive metrics, and data points linked to the expected outcomes.

Choose

Weigh Product and Technology Options Analyze and compare technology and vendors for – hardware (sensors, actuators, etc.), connectivity, IoT platforms, data platforms, analytics platforms, and security solutions.

Make Build vs. Buy DecisionsDepending on the line of business and criticality of data, some organizations might want to build core systems in-house, while others might be well-suited with off-the-shelf systems. It is essential to scrutinize build vs. buy decisions across the stack closely before embarking on implementation plans.

Choose Partners for BuyProducts and services should be run through detailed analysis, especially where vendors showcase their industry domain and IoT expertise along with their ecosystem partnerships. Depending on the case, a comparison needs to be made between niche vendors and vendors with end-to-end competencies.


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Implement and Interpret

Get the Skills on Board Given the complexity of the IoT architecture, enterprises need to bring mechanical, software, embedded and data science skills for IoT implementation. The requirement for product engineering or system integration across layers will vary based on the build vs. buy decisions.

Implement and Integrate Build a comprehensive roll-out plan with a phase-wise implementation schedule, taking into consideration the integration efforts with the existing systems.

Get Actionable Insights from IoTEnterprises need to manage the IoT data and build analytics on top of it to get actionable insights. IoT data analysis is expected to drive transformations and unveil opportunities for enterprises, and thus should be a core part of IoT implementation.

Manage

Manage the Connected SystemPost implementation, each layer of IoT should be regularly monitored and maintained in order to ensure optimum resource utilization and continued level of output from the system.

Upgrade Incrementally With changing IoT landscape and business requirements, enterprises will need to upgrade their technology. This should be done incrementally to ensure seamless migration.


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CASE STUDY: END-TO-END IoT SECURITY FOR A LEADING MOBILE TECHNOLOGY COMPANY

Where We StartedA mobile technology company, powered by its suite of IoT solutions, enables interoperability for various IoT devices, networks, and industries through global standards such as OneM2M. The company was relying on their own hop-by-hop security implementation based on PSK certificates. However, the implementation had limitations and was not scalable. The PSK certificates, stored in a file or file system, could easily be stolen from a compromised device / server. There was no provision for automatic security credential configuration. And it was not easy to identify and block a compromised device, making the whole system vulnerable.

What We BuiltHARMAN Connected Services and Entrust Datacard have forged a strategic partnership to build ioTrust Security Solution, with a common goal of establishing trust within the IoT infrastructure and securing the interaction between users, devices, and systems. The Entrust Datacard ioTrust Security Solution offers pre-integration of identity management and data security into IoT devices. ioTrust Security Solution, based on enterprise grade encryption technologies, establishes trusted identities for devices across IoT infrastructure, creating secure ecosystems for data transmission. The solution includes the following security elements as standards – Endpoint Data Security, Device Identity Management, Device Discovery, Embedded Trust, Self-provisioning Infrastructure, and Seamless Integration.

The implementation of Security Solution, in this case, involves supporting security between HARMAN IoT Gateway and the customer’s cloud infrastructure via SSL sockets created using Entrust Datacard managed identities. The overall goal was to have the customer’s IoT solutions work along with strong security from Entrust Datacard and HARMAN’s IoT suite with added analytics services to provide automation, efficiencies, and insights. The key highlights of the solution are as follows:


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• Entrust Datacard ioTrust security solution offers automatic security credential configuration, i.e., certificates that are pre-configured or configured to gateways / devices.

• ioTrust Security Solution identities are safely stored in a soft key-store instead of on a file, which significantly reduces the possibilities of security credentials from being compromised.

• The solution allows upgrading to the hardware security provided by Trusted Platform Module.

• ioTrust Security Platform provides a scalable solution, wherein security credentials for a large number of devices can be easily managed.

• In the less probable case of a security breach, the end devices can also be easily blocked by identity revocation mechanism without impacting other modules, devices, and operation.

• Each device / gateway is identified using a unique certificate, where the server and the device can mutually authenticate each other before establishing the data connection.


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ABOUT THE AUTHORS

Ajay Phatak is a Vice President at Connected Services division of HARMAN International. With over 30 years of experience in technology development and deployment in key verticals of industrial and healthcare, he has been working on IoT products and solutions for more than a decade. He has worked in devices, communication, control, remote management, edge analytics, software and real-time systems – delivering connected IoT solutions to many customers over this time. Ajay holds a Bachelor’s degree in Chemical Engineering from IIT Bombay and Masters in Science degree in Computer Science from Savitribai Phule Pune University.

Nikhil Kulkarni is an Engagement Lead with Zinnov. Having seven years of rich experience in management consulting, he has advised leadership teams of clients in the formulation of business and operations strategy. He has spearheaded multiple client engagements in strategy, consulting, and sales enablement for global product engineering service providers. Nikhil has deep expertise in various hi-tech industries including Software & Internet companies. Previously, Nikhil spent over five years working across Hewlett-Packard & Fujitsu Consulting. Nikhil has an MBA from Symbiosis Institute of Business Management, and a Bachelor’s degree in Electronics Engineering from the University of Pune.

Ketan Vaid is Senior Consultant with Zinnov and has over six years of experience in multiple domains including management consulting, private equity, and IT. Ketan has helped global clients create value through the formulation of innovative and accelerated growth strategy. With a keen interest in upcoming technologies, Ketan has also twice fronted Zinnov’s IoT technology services report rating service providers’ competency. Ketan holds an MBA from Ross School of Business, University of Michigan and from CUHK Business School. He has his Bachelor’s degree in Computer Science from JUIT, India.

ABOUT ZINNOV

Zinnov was founded in 2002 and is headquartered in Bangalore, with a presence in Gurgaon, Silicon Valley, and Houston. Since its inception, Zinnov has built in-depth expertise in Product Engineering and Digital Transformation. They assist their clients by:

• Research and strategy consulting for software service providers in the areas of Product Engineering and Digital Transformation

• Enabling companies to develop and optimize a global engineering partner strategy to achieve higher throughput, innovation, productivity, and cost savings

• Growing revenue for company’s products and services in India and other emerging markets

• Helping MNC GICs to consolidate their geographic footprint

With their team of experienced professionals and research teams, Zinnov serves clients across software, semiconductor, consumer electronics, automotive, storage, telecom & networking, healthcare, banking, financial services, and retail verticals in the US, Europe, Japan, and India.

For more information, visit http://zinnov.com

Partner with an industry expert

HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things. With leading brands including AKG®, Harman Kardon Infinity®, JBL®, Lexicon®, Mark Levinson® and Revel®, HARMAN is admired by audiophiles, musicians and the entertainment venues where they perform around the world. More than 50 million automobiles on the road today are equipped with HARMAN audio and connected car systems. Our software services power billions of mobile devices and systems that are connected, integrated and secure across all platforms, from work and home to car and mobile. HARMAN has a workforce of approximately 30,000 people across the Americas, Europe, and Asia. In March 2017, HARMAN became a wholly-owned subsidiary of Samsung Electronics Co., Ltd.

Visit our website at: https://services.harman.com/

http://zinnov.com

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MOVING BEYOND PROOF OF CONCEPT (PoC) IN IoT

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