Introduction to IoT Systems

Refers to the interconnected components like sensors, actuators, cloud services, protocols, and layers that make up IoT systems.

A framework that divides IoT systems into layers, helping to manage, monitor, and ensure system stability.

It involves the flow of data from sensors or devices to the cloud, where it gets processed, analyzed, and stored.

This layer is responsible for collecting raw data from sensors, RFID tags, and other devices using wireless technologies.

This layer focuses on routing the collected data to the next layer using network protocols.

This layer stores and processes the data collected from lower layers, helping to make decisions based on the information.

This layer focuses on the application of IoT data, ranging from home automation to health monitoring, and user interface design.

This layer analyzes data from the lower layers and decides future actions based on that information.

The process of rapidly iterating on designs using prototypes to gather feedback and improve a product. This is crucial in IoT development, where devices might be difficult to upgrade.

A hardware unit that can sense its environment and/or perform tasks in its environment. It typically has a microcontroller, power source, sensors, actuators, and communication capabilities.

Devices that provide basic sensor readings, actuation tasks, and sometimes limited user interaction.

A software platform that manages the lifecycle of applications on an IoT device. It provides APIs for developers to interact with the device and its functionalities.

A common set of rules and specifications that ensure different IoT devices and systems can communicate and interoperate with each other.

A group of companies working together to create agreed-upon standards for IoT technologies. This helps to ensure interoperability and reduces fragmentation in the IoT ecosystem.

The ability of different IoT devices and systems to work together seamlessly, regardless of the manufacturer or technology used.

A process of configuring and setting up an IoT device for use. It includes tasks like assigning an identity, connecting to a network, and installing necessary software.

Devices that collect data from their surroundings (sensors) or send data to their surroundings (actuators). They must be uniquely identifiable with an IP address for easy recognition on a network. They are active, collecting real-time data, and can operate independently or be user-controlled.

The brain of the IoT system, responsible for processing raw data from sensors to extract valuable insights. It provides intelligence to the data by analyzing it in real-time and securing it through encryption. Embedded hardware devices like microcontrollers perform this processing.

They connect various parts of the IoT system, acting as a bridge for communication. Gateways route data to the appropriate location for analysis and utilization, ensuring data flow and network connectivity, crucial for any IoT system.

Applications form the user-facing aspect of the IoT, utilizing the collected data to deliver services. They are cloud-based programs that provide meaningful insights and functionality based on the data analyses. They are controlled by users and often provide tangible outputs like home automation, security, or industrial control.

This crucial principle involves understanding the needs and problems of users. Designers should focus on how their IoT product addresses those needs, providing valuable solutions that make a difference in people's lives.

IoT products should be designed with security as a top priority. Security measures like encryption, authentication, and access control are essential to safeguard user data, privacy, and the overall integrity of the system.

IoT products should be designed to be intuitive and user-friendly, minimizing complexity and maximizing usability. User interfaces, controls, and interactions should be simple and clear, ensuring a seamless and enjoyable experience.

Sustainability is crucial for any IoT project. Consider long-term aspects like energy efficiency, material usage, and ethical considerations during the design process. The goal is to create products that minimize their impact on the environment and promote responsible development.

The initial stage where data is produced by a device, system, or their interactions. It can be generated actively (e.g., sensor readings) or passively (e.g., system log events). The frequency of data generation depends on the device capabilities and application needs.

The collection of generated data from devices or systems. This involves communication with distributed devices over wired or wireless links, respecting security, protocol, and application requirements.

Ensuring the accuracy and meaningfulness of data acquired from devices or systems. It typically involves applying rules, semantic annotations, or other logic to verify data integrity.

The storage of vast amounts of data generated by M2M interactions. This data, often referred to as 'Big Data,' requires efficient storage solutions for further processing and analysis.

The process of manipulating data that is either at rest (stored) or in motion (streaming), enhancing it for future use. This can include cleaning, transforming, or aggregating data.

The persistence of data even after it's erased or removed from storage. It refers to the possibility of recovering deleted information, posing security risks. Various techniques such as overwriting, degaussing, encryption, and physical destruction are used to mitigate this risk.

The analysis of stored data to extract meaningful information and support decision-making processes. It involves applying techniques to understand patterns, trends, and insights from the data.

A model where all services and applications can be accessed on the internet upon request, encompassing various components like software, platforms, infrastructure, data, security, communication, etc.

A key concept in IoT where intelligence and computational processing are distributed across the network, including edge nodes like devices themselves, instead of solely relying on centralized enterprise systems.

A technique to minimize communication between devices and enterprise systems by only transferring relevant data, instead of processing all incoming data at the enterprise level.

A key advantage of distributed business processes, where devices utilize their own resources (like memory and multi-core CPUs) to perform tasks and make decisions, reducing reliance on centralized systems.

An approach where enterprise systems would struggle to handle the massive amount of data generated by IoT devices, leading to overloading and inefficient processing.

A process where enterprise systems attempt to process all data from devices, even if it is non-relevant or minor, causing potential strain and inefficiency.

A key benefit of distributed business processes, where devices can perform more complex actions based on the information they gather and process, leading to smarter and more efficient operations.

A shift from centralized intelligence to decentralized intelligence, where devices can make decisions based on local data and perform more complex actions, reducing reliance on enterprise systems.

The ability for consumers to request and release computing resources like server time and storage on demand, without manual intervention.

The service provider pools numerous physical and virtual resources to serve many customers, dynamically assigning and re-assigning them based on demand.

Cloud services are accessible over various networks through standard mechanisms, allowing them to function on a range of devices, from thin clients to powerful workstations.

The consumer is not aware of the physical location of the resources but can specify it at a higher abstraction level. (e.g., country, state, or data centre.)

A cloud service model where software is delivered to users on demand, typically through a web browser. The user does not manage the infrastructure, and it is handled by a service provider. Examples include online office suites and CRM tools.

The act of automatically monitoring, managing, and optimizing resource usage in cloud systems. It involves metering resources like storage, processing, bandwidth, and active user accounts.

Cloud computing offers various service models and deployment options depending on the enterprise's needs and preferences.

Software as a Service (SaaS) allows consumers to access and use software applications through a subscription-based model. End-users don't manage the cloud infrastructure.