Ubiquitous Computing, Embedded and Distributed Intelligence

- Overview

With the rapid development and increasing complexity of computer systems and communication networks, users have higher and higher requirements for embedded and pervasive computing. Therefore, traditional computing technologies may not be able to meet user needs in open, dynamic, heterogeneous, mobile, wireless, and distributed computing environments, which is a great challenge. Therefore, we need to build embedded systems and networks based on ubiquitous computing.

Embedded ubiquitous computing promises to improve people's quality of life by creating new applications based on data processing in IoT networks. Much research work has been carried out on novel processing and communication architectures, techniques and management strategies. 

Embedded ubiquitous computing systems can utilize wireless sensor networks to collect and process data, and use cloud technology, peer-to-peer systems, and big data paradigms to provide computing and analysis capabilities.

Today, due to the development of smart devices, embedded ubiquitous computing technology, and many smart services combined with the network world, the Internet of Things (IoT) has become more promising to realize various embedded applications, allowing users to enjoy more comprehensive services. 

As an emerging research topic, embedded ubiquitous computing involves and supports a larger vision of computing, including smart devices (mobile, wireless, services), smart environments (embedded system devices), and smart interactions (between devices).

- IoT, Pervasive and Ubiquitous Networks

The Internet of Things (IoT) can be defined as a pervasive and ubiquitous network which enables monitoring and control of the physical environment by collecting, processing, and analyzing the data generated by sensors or smart objects. 

In reality, Machine-to-Machine (M2M) can be viewed as a subset of the IoT. The IoT includes Machine-to-Human communication (M2H), Radio Frequency Identification (RFID), Location-Based Services (LBS), Lab-on-a-Chip (LOC) sensors, Augmented Reality (AR), robotics and vehicle telematics. 

Many of these technologies are the result of developments in military and industrial supply chain applications; their common feature is to combine embedded sensory objects with communication intelligence, running data over a mix of wired and wireless networks. 

- Embedded and Distributed Intelligence

Embedded and distributed intelligence capabilities in the network are core architectural components of the IoT for three main reasons:

• Data Collection: Centralized data collection and smart object management do not provide the scalability that the Internet requires. For example, the millions of sensors and actuators in a smart grid network cannot be effectively managed using a centralized approach.
• Network Resource Conservation: Since network bandwidth may be scarce, collecting environmental data from a central point in the network inevitably results in the use of a large amount of network capacity.
• Closed-loop functionality: For some use cases, IoT requires reduced reaction times. For example, sending an alarm over multiple hops from a sensor to a centralized system (running analytics) before sending a command to an actuator would result in unacceptable delays.

This distributed intelligence capability is known as fog computing, an architecture specifically designed to process data and events from IoT devices closer to the source rather than a central data center (also known as the "cloud"). 

In conclusion, fog computing is an extension of the cloud paradigm. It's similar to cloud computing, but closer to the ground. Fog computing architectures extend the cloud to the physical world of things.