Our cybernetic process automation framework (the Mantra M5) and our smart products and services will require the use of electronic componentry for embedded systems to enable the integration of the cyber-physical systems. This would include electronic components such as processors, memory, sensors, actuators, displays, power sources, communication protocols, and other electronic components.

TABLE OF CONTENTS

• Guiding Principles
• Managing the IDE process

GUIDING PRINCIPLES

Herewith is our guiding principles for enabling smart management utilizing PCBs and other electronic components:

1. Multimodal Sensing Capability – This principle refers to the ability to collect data from different types of sensors and sources, and to integrate and analyze this data to gain insights and make data-driven decisions.
2. Appropriate Data Management – This principle refers to the need for secure, scalable, and compliant data management practices. This includes data storage, data cleaning, data transformation, and data analysis.
3. Needs Based Functionality – This principle refers to the importance of tailoring electronic components and systems to specific industrial needs and requirements. This may involve customizing hardware and software components, or integrating off-the-shelf components to meet specific needs.
4. Security and Scalability – This principle refers to the need for secure and scalable electronic components and systems. This includes implementing appropriate security measures, such as encryption and access controls, and designing systems that can scale to meet changing business needs.
5. Reliability and Dependability – This principle refers to the need for electronic components and systems that are reliable and dependable in industrial settings. This may involve using high-quality components, designing for robustness and durability, and implementing appropriate maintenance and support procedures.
6. Interoperability: This principle refers to the ability of electronic components and systems to work together seamlessly, regardless of their source or vendor. This requires standardized communication protocols and interfaces, and a commitment to open standards.
7. Human-Centered Design: This principle refers to the importance of designing electronic components and systems with the needs and abilities of human users in mind. This includes designing user interfaces that are intuitive and easy to use, and providing appropriate training and support for users.
8. Continuous Improvement: This principle refers to the need for ongoing improvement and optimization of electronic components and systems. This may involve using feedback from users and stakeholders to identify areas for improvement, or using data-driven approaches to optimize performance and efficiency over time.

Here are detailed descriptions for each of them.

1. MULTIMODAL SENSING CAPABILITY – Data will be collected based on different organs of interaction. Multi-modal sensing refers to the ability to collect data from multiple sensors that capture information from different modalities, such as visual, auditory, tactile, or olfactory. By combining data from multiple sensors, it is possible to obtain a more comprehensive view of the monitored environment or activity. This aggregation is key for our solutioning for actionable intelligence.

2. APPROPRIATE DATA MANAGEMENT based on decentralizing edge monitoring and centralized data engineering. The electronic componentry would enable us to create a decentralized, automated, and secure system that would allow us to generate real time data, monitor the machines and data, and control the processes within the factory in an industrial setting. This would allow us to maximize efficiency and minimize downtime.

3. NEEDS BASED FUNCTIONALITY – The electronic components should be selected based on the specific needs and the use cases that need to be addressed. For a factory setting, this would include the selection of components that are compatible with the IIoT system, and the selection of components that are suitable for the environment. The components should also be compatible with the existing IT infrastructure, and should be able to integrate with the current systems.

4. SECURITY AND SCALABILITY  when embedding sensors in an IoT setting:

5. RELIABILITY AND DEPENDABILITY – The electronic components should also be reliable and dependable, as tthey will be the backbone of the factory. The components should also be cost effective, as this will help to keep the cost of the system low. The components would also be able to withstand extreme temperatures and vibrations, making them suitable for the industrial environment. Considerations for military, commercial and other (first responder for example) should also be part of the process based on the use case. This would necessitate the components be of high quality, as this will ensure that they are able to withstand austere conditions in military use and the rigors of the industrial environment.

6. INTEROPERABILITY – Interoperability is a critical aspect of the Industry Internet of Things (IIoT) ecosystem, especially when it comes to sensors. In the context of IIoT, interoperability refers to the ability of different devices, systems, and platforms to communicate and exchange data seamlessly, regardless of their make or model. Specifically, for sensors, interoperability refers to the ability of sensors from different vendors, with different communication protocols, to communicate and share data with each other.

The lack of interoperability among sensors is a major challenge in the IIoT ecosystem. This is because sensors from different vendors often use different communication protocols, which can result in a lack of standardization and compatibility between different systems and devices. This can lead to data silos, where data is trapped within individual systems, making it difficult to share and analyze data across different platforms.

To address this challenge, several standards and protocols have been developed to promote interoperability among sensors in the IIoT ecosystem. One such standard is the OPC Unified Architecture (UA), which is an open standard for industrial interoperability that provides a common platform for data exchange and communication between different devices and systems. Other standards include MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol), which are lightweight protocols that enable sensor devices to communicate over the internet.

In addition to these standards, there are also several middleware solutions that can help to promote interoperability among sensors in the IIoT ecosystem. These solutions act as a bridge between different systems and devices, enabling them to communicate and share data seamlessly. Some examples of middleware solutions include Apache Kafka, Azure IoT Hub, and AWS IoT Core.

By promoting interoperability among sensors in the IIoT ecosystem, it becomes possible to create a unified data environment where data can be shared and analyzed across different systems and platforms. This enables organizations to gain a more comprehensive view of their industrial processes, make data-driven decisions, and optimize their operations for maximum efficiency and profitability.

7. HUMAN-CENTERED DESIGN – Human-Centered Design (HCD) is an approach to product and system design that focuses on creating solutions that meet the needs, goals, and behaviors of end-users. In the context of Industry 4.0 and sensors, HCD is crucial for designing solutions that are intuitive, user-friendly, and efficient, while also being capable of providing actionable insights.

HCD involves several key principles that can be applied to the design of sensors and other IIoT technologies. These include:

1. User Empathy: The first step in HCD is to understand the needs, goals, and behaviors of end-users. This involves conducting user research, observing user behavior, and gathering feedback to gain a deep understanding of the user’s perspective.
2. Iterative Design: HCD involves an iterative design process that involves prototyping, testing, and refining the design based on feedback from users. This allows designers to create solutions that are tailored to the specific needs of the end-users.
3. Collaborative Design: HCD involves collaboration between designers, engineers, and end-users to ensure that the final solution meets the needs of all stakeholders. This involves working closely with end-users to co-create solutions that meet their needs.
4. Usability: HCD focuses on creating solutions that are intuitive and easy to use. This involves designing interfaces that are user-friendly, providing clear feedback to users, and minimizing the cognitive load required to use the system.
5. Accessibility: HCD involves designing solutions that are accessible to all users, including those with disabilities. This involves designing interfaces that are easy to read, providing alternative input methods, and ensuring that the system is compatible with assistive technologies.

By applying HCD principles to the design of sensors and other IIoT technologies, it becomes possible to create solutions that are tailored to the needs of end-users. This not only improves the user experience but also ensures that the technology is used to its full potential, providing actionable insights and enabling organizations to optimize their operations for maximum efficiency and profitability.

8. CONTINUOUS IMPROVEMENT – Continuous improvement is an essential concept in operational management that involves continually reviewing and improving processes and systems to optimize efficiency, reduce waste, and improve quality. In the context of sensors and the shop floor, continuous improvement involves using real-time data from sensors to identify areas of inefficiency and waste and making incremental improvements to the production process.

Continuous improvement in the context of sensors and operational management on the shop floor involves several key steps:

1. Data Collection: Continuous improvement requires data to identify areas for improvement. Sensors can be used to collect real-time data on machine performance, production rates, and quality metrics.
2. Data Analysis: The collected data is then analyzed to identify areas of inefficiency, waste, and quality issues.
3. Improvement Planning: Based on the analysis, improvement plans are developed, prioritizing the areas that will have the greatest impact on production efficiency, waste reduction, and quality improvement.
4. Implementation: The improvement plans are then put into action, and the sensors continue to collect real-time data to monitor the impact of the changes.
5. Continuous Monitoring and Feedback: The improvement process is ongoing, with continuous monitoring and feedback on the impact of the changes made. Further adjustments are made based on the data collected, and the cycle continues.

By utilizing sensors and real-time data, continuous improvement on the shop floor becomes much more efficient and effective. The ability to monitor production performance in real-time allows for more timely and targeted improvements, reducing downtime, minimizing waste, and improving overall quality. The result is a leaner, more efficient operation that can quickly adapt to changing market demands.

MANAGING THE IDE PROCESS

The code base for sensor sketch-ups is different from managing other types of code in an Integrated Development Environment (IDE) because the primary focus is on hardware interactions and data collection, rather than software functionality. Sensor sketch-ups involve working with microcontrollers and sensors, such as Arduino boards and various sensors, to collect data and control hardware devices.

Some key differences in managing sensor sketch-up code compared to other types of code in an IDE include:

1. Hardware Interactions: Sensor sketch-up code requires interaction with physical devices and hardware components. This means that the code needs to be written with a strong understanding of the hardware being used and its associated programming libraries.
2. Real-time Data Collection: Sensors are often used to collect real-time data from the environment or machinery. The code needs to be optimized for efficiency to ensure that data is collected quickly and accurately.
3. Limited Resources: Microcontrollers used in sensor sketch-ups have limited resources, such as processing power and memory. The code needs to be written with this in mind to ensure that it does not overwhelm the microcontroller or interfere with its other functions.
4. Debugging Challenges: Debugging sensor sketch-up code can be more challenging than traditional software development since it involves physical hardware. It can be difficult to isolate issues that arise and determine whether they are caused by the hardware or the software.
5. Specialized Tools: Sensor sketch-up code is often written using specialized tools and libraries that are specific to the microcontroller and sensors being used. These tools may not be available in a traditional IDE.

Overall, managing code for sensor sketch-ups requires a different approach than managing other types of code in an IDE. It involves a strong understanding of hardware interactions, optimization for real-time data collection, and specialized tools and libraries.

NI+IN UCHIL Founder, CEO & Technical Evangelist
nitin.uchil@numorpho.com