The Internet of Things (IoT) is a network of physical objects (sensors, devices, vehicles, buildings, etc.) embedded with electronics, software, sensors and network connectivity that are designed to collect data and create business value by connecting the dots. Enterprises are adopting the IoT for additional revenue generation from IoT-enabled products/ services, operations optimization, cost reduction, improved efficiency and customer experiences.
IoT development and adoption are driven by multiple factors, including readily available low-cost sensors, increased bandwidth and processing power, wide-spread usage of smartphones, availability of big data analysis tools and scalability of cloud services. Unlike typical application, IoT has a very dynamic environment with millions of sensors and different devices used in conjunction with intelligent software. Huge volumes of data generated across a smart ecosystem add great technical complexity, and make IoT applications uniquely different with following factors:
- Combination of hardware, sensors, connectors, gateways, & application software in a single system
- Real-time streaming analytics / complex event processing
- Support for data volume, velocity, variety, & veracity
- Visualization of large-scale data
- Server or cloud services agnostics
Navigating these complexities requires careful planning, domain knowledge and rigorous implementation. Based on Gadgeon’s work with clients, we have identified five essential requirements that should be part of every IoT implementation:
- Edge processing: Data needs to be gathered by sensors and analyzed in real-time. This allows for rapid response to sudden change, such as spotting a grocery freezer compressor failure or identifying a medical device malfunction.
- Data ingestion: Processes need to be in place for collecting data from multiple devices and sensors and transforming it for use by cloud-based applications.
- Device management: Need to ensure that IoT devices are provisioned securely, communicate efficiently and can be updated with accelerated and agile approaches.
- Data Analytics: Deep dives into IoT data should result in cost savings, as well as insights to create new products and new revenue models.
- Integration with enterprise systems: IoT insights need to be delivered to enterprise systems and receive reference metadata in order to interpret device data.
Edge processing involves the computation and analysis of data on distributed devices positioned at the edge of a network rather than on centralized systems. This includes both local sensors that gather data, and edge gateways that process it. The advantage of an edge computing architecture is that data can be analyzed close to where it is captured, resulting in faster response to changing conditions. Additionally, edge gateways can transform proprietary or legacy protocols into IoT protocols for transmission to existing corporate networks or the cloud. In addition to performing edge analytics, the gateways can also pre-process and filter data to reduce transmission, processing and storage costs, as well as send commands to IoT devices and perform software upgrades.
STRATEGY AND GUIDELINES
- Fully assess lifetime device costs, being sure to include operational overhead expenses, such as monitoring, upgrades and power requirement. Adjust planning to ensure such costs do not outweigh lifetime value.
- Create policies to secure devices with appropriate firewalls and hardened operating systems. Use digital signatures to protect the code and algorithms. Encrypt data at rest and in transit.
- Aggregate data before transmission to maximize bandwidth. Send changed data only, using the proper encoding format and packet size. Use separate frequencies depending on how critical the data is.
- Implement retry and network availability patterns to detect and prevent failures when sharing data with external systems. This practice is also useful when edge gateways have only intermittent connectivity with on-premise or cloud platforms.
- Finalize the real-time analysis which are most time-critical for your business and perform them at the edge to allow immediate action.
Data ingestion deals with device telemetry data being imported and converted into a format usable by cloud based IoT services. Stream processing normalizes the data into a common data model. Notification services and message buses inform business applications and users to conditions that require action, such as an alarm triggered by a freezer that can’t maintain the desired temperature range.
STRATEGY AND GUIDELINES
- Assess the expected data message size, criticality and required response time to ensure the cloud components can process the data required to meet business KPIs and IoT goals.
- Send telemetry data about business conditions on a dedicated, higher-bandwidth channel rather than through a channel used for less critical log files. Doing so will reduce bandwidth requirements and related costs while ensuring business objectives are met.
- ache frequently used data so it doesn’t have to be fetched repeatedly from a remote source. This will maximize performance and minimize network costs.
- Evaluate regulatory requirements to ensure ingested data is stored in compliance with government or industry regulations.
- Configure the gateway and platform hardware with ample computing and storage capacity to perform protocol conversion.
- Provide load balancing, horizontal autoscaling and failover data processing to ensure consistent, high-performance data ingestion.
- Assess the server agnostics requirement and finalize the selection of cloud services provider along with micro-service-based orchestration framework with required platform services.
Device management covers the hardware, software and processes that ensure devices are properly registered, managed, secured, upgraded, and notified if a device fails. The IoT implementer will need to account for these functions, even if the cloud provider doesn’t offer the required device management components. Comprehensive device management enables connected devices to easily and securely communicate with other devices and cloud platforms, while helping the enterprise reliably scale to millions of connected devices and billions of messages.
STRATEGY AND GUIDELINES
- To help ensure performance and reliability, consider monitoring device “heartbeats” with services such as the native message broker in AWS IoT Core, which creates a separate channel that confirms connectivity with the cloud platform.
- Create dedicated channels and processes for various types of device data to make the best use of available bandwidth and increase scalability.
- For increased reliability, configure a dedicated, persistent, bi-directional channel that sends device commands even if other communication channels fail. To more quickly add new devices to the network, enable auto-registration through validation with a trusted system, such as a network management platform.
- Create an abstraction layer that allows for greater automation of processes to reduce management costs.
- Use a content delivery network to speed the delivery of device software updates.
With descriptive (cold path) analysis, large amounts of data are analyzed by advanced algorithms after the data is stored on the cloud platform. Such analysis can uncover trends or corrective actions needed to improve the business or customer experience. Unlike streaming analytics (hot path) that apply relatively simple rules to data in real time for short-term actions (i.e., detecting fraud, security breaches or critical component failures), cold path analysis involves more sophisticated big data analytics, such as machine learning and AI, being applied to provide deeper data insights.
STRATEGY AND GUIDELINES
- To drive the most insights from data, consider using a complex event processing framework that combines data from multiple sources, such as enterprise applications and IoT devices, to dynamically define and process analytical rules by inferring meaning from complex situations.
- Aggregate data before rather than during analysis to improve processing speed.
- Use a data lake, which stores data in its native format, to consolidate enterprise data for easier access.
- Categorize telemetry data by each variable, such as message size and the receiving application’s needs, to speed access.
- Consider creating data services to make it easier for users to access data on demand.
Integration with Enterprise Systems
Integration with business applications and enterprise systems enables the sharing of raw and processed data, as well as analysis-driven insights. With deep enterprise integration, the IoT architecture can deliver benefits such as improved efficiencies, reduced costs, increased sales, heightened customer satisfaction and the ability to create and lead new markets. To share data and insights, businesses need mechanisms such as application programming interface (API) gateways, service buses and custom connectors.
STRATEGY AND GUIDELINES
- Evaluate communication needs to choose the best approaches, such as simple message broker, request/response and data-level integration, based on data volumes, performance requirements and the integration needs of downstream systems.
- Provide self-service APIs to develop an ecosystem that enables integrators and developers to consume data and business insights.
- Use RESTful APIs to provide on-demand sharing of data in various formats and among disparate systems.
- Design APIs that make it easier for mobile applications to consume and access operational data from IoT devices anywhere and at any time.
- Create high data-ingestion queues on the cloud IoT platform to swiftly pass large volumes of data from IoT devices to downstream applications.
Every IoT implementation will be distinct in its own way, depending on each business’s requirements, expected outcomes, levels of IoT and data skills, and technology infrastructure maturity. In all cases, however, the above mentioned five requirements are essential to ensuring a successful IoT implementation, with minimal cost and delay. Gadgeon - IoT software development company key learnings from recent IoT implementations are given below
- Organizational and cultural change is often underestimated - this is the number one challenge we hear about when we talk to end-users who have implemented IoT projects and ask them about their biggest learning. Take the organizational change management efforts seriously, start early and use agile methods wherever possible!
- IoT projects take much longer than anticipated – based on our experience that the fastest IoT technology implementations went from business case development to commercial roll-out in 9 months. Teach all stakeholders to be patient and build in little success stories that help satisfy stakeholders and senior management on the way.
- Necessary skills are not available in-house - end-to-end IoT solution development requires a broad range of skills including embedded system design, cloud architecture, application enablement, data analytics, security design and back-end system integration (e.g., into ERP/CRM). Map the IoT skill gaps, cross-train and upskill the workforce with a focus on new technologies unique to IoT. Work with true IoT technology experts from different fields with deep domain knowledge.
- Security is often an afterthought - the security features are commonly cut from initial designs to accommodate additional functionality. However, global data and device security needs to play a central role in IoT technology development, for companies and customers to broadly adopt the ‘Internet of Things’. Follow security best-practices such as employing a secure boot process or using unique identity keys and map the attack surface.
- Data collection and interconnectivity issues are a major complexity driver - when you download an app on your phone, you take for granted that it is ready to be used within seconds. People who are not too familiar with IoT often have the misconception that it must be similar with IoT technology. However, today’s reality is far from that. Data collection and protocol translation still takes up a majority of today’s IoT development efforts.
- Scalability becomes an issue when going to thousands of devices - a large manufacturer of construction equipment initially created a few neat dashboards to monitor their machines remotely. A year later, the project was amended to start performing fault analysis and predictive maintenance of the hydraulic systems. That is when the team realized that the data model did not support the necessary backend processing capacity. Even though you should start small, you must think big from the beginning. Build your IoT technology using micro-service architecture and challenge your hardware design and data model.
About Gadgeon Systems
Gadgeon is an IT outsourcing services Partner in the Digital Transformation Journey of our customers. From sensor-node to Gateway to Cloud application to mobile App, Gadgeon can architect and design your entire IoT Solution. Our extensive experience in end-to-end IoT, IIoT, M2M technologies, network/communication technologies, application management, cloud, analytics, and automation frameworks enable our customers in building new business models and realizing value from their IoT implementation and digital transformations. Our clients include the world’s leading Home/Building Automation, Telematics and Communication Service Provider companies. Let us be a part of your IoT Solution. To explore how Gadgeon can help you realize your vision, act now to speak with our expert team.