Additional

The network layer is divided into two sublayers: routing layer which handles the transfer of packets from source to destination, and an encapsulation layer that forms/creates the packets.

• RPL Protocol: distance-vector protocol, builds a directed graph

• CORPL Protocol: extension of the RPL protocol for cognitive networks

• CARP Protocol: distributed routing protocol that allows multiple hosts on the same network segment to share an IP address

• 6LoWPAN: lightweight IP-based communication to travel over low data rate networks, has several security issues

• IPv6 over Bluetooth Low Energy

• Connecting Devices

• Sensors and Actuators

• Connectivity

• Internet

• Communication Protocols

• Data Processing

• Cloud Computing

• Edge Computing

• Data Analytics

• Data Security

• Privacy Concerns

4 C’s, 3 D’s, SPEI

• MQTT (Message Queuing Telemetry Transport) -> lightweight, publish/subscribe with brokers

• CoAP (Constrained Application Protocol) -> for limited networks

• HTTP (Hypertext Transfer Protocol) -> popular in WWW, but not optimized for IoT

• WebSocket -> established TCP connection

• DDS (Data Distribution Service)-> publish/subscribe, but no brokers

Choose standards wisely and implement them correctly

• Scalable infrastructure for data storage, processing, and analysis

• Scalability and elasticity

• Cost-efficiency

• Seamless connectivity and integration

• Volume and diversity of data generated by IoT devices presents risks in data access and integrity

• Most important measures

  • Authentication

  • Data encryption (data at rest and during transmission)

  • Continuous monitoring

  • Regular updates

• Collection of personal information (e.g. location and health data) at scale presents risks for violations

• Most important measures

  • Transparency and informed consent

  • Data minimization

  • Anonymization and pseudonymization techniques

• IoT data can be processed, analyzed, and transformed into actionable intelligence, enabling organizations to optimize operations

• Real-time analysis

• Predictive analytics, next step is Prescriptive Analytics

• Each protocol is designed to cater to specific requirements, such as data size, power consumption, network bandwidth, and the context in which the devices operate

• Ensure that devices can exchange data efficiently and effectively

• Examples: MQTT, CoAP, HTTP, WebSocket, DDS

• Data packet size is considerably smaller, since the header is smaller

• No encrypting/decrypting needed, since MQTT can be any type of data (HTTP only text)

• Transfer for multiple messages is faster, since the connection doesn’t need to be re-established (MQTT protocol can keep a connection open)

• Connection Pool: enabling bidirectional communication for many devices connected by a single entity. Also, MQTT enables real-time communication as a sequential hierarchy does not lead it. HTTP must work off each request in hierarchical order – first come, first serve.

• provisioning of intent-based policies

• application of business intent to network configuration

• verification of configuration before devices are deployed

• end-to-end verification of network-wide behaviour

• Optimizing Operational Efficiency: identify issues leading to inefficiencies -> optimize

• Reducing Costs: e.g. reducing energy consumption

• Enhancing Customer Experience: analyse customer data

• Improving Safety: identify safety hazards -> preventive measures

• Scaling Problem

• Volatility of Data

• Network Level

• Real-time analyis of streaming data

• Network analytics

• Volume of Data: IoT devices generate massive amounts of data with which relational databases might struggle

• Vertical vs. Horizontal Scalability: Getting relational databases to scale horizontally is complex => the system must be equipped with more power, no additional systems possible

• Concurrency: If many devices send data at the same time, performance bottlenecks might occur

• Data Variety and Velocity: IoT data can be highly heterogeneous, arriving at high velocities. Relational databases are not optimized for that

• Time-Series-Data: IoT data is often time-series data, for which relational databases are not optimized

• Unreliable Networks: IoT devices often operate in low-bandwidth, intermittent, or disconnected environments, making it difficult to establish a reliable connection with a central database server, which is required by relational database systems

• Data Integrity: Relational database systems rely on data integrity, which might be difficult to achieve

• Insufficient Performance: Real-time processing requires low-latency, high-speed data processing that is often not possible with traditional relational database systems

• Complex Event Processing (CEP): CEP is often not supported

• Time-Series-Data: IoT data is often time-series data, for which relational databases are not optimized

• Data Preprocessing: Since relational databases require data to satisfy a schema, data might need to be pre-processed to fit into the database

• Network Traffic: Network data has a high volume and variability for which relational databases are not suited

• Sensor: connected to the power circuit; collects information

• Actuators: create physical movements

• Power Supply: can be a battery, cell, AC, or DC power; creates a power circuit when in a closed loop

• Relay: electrically operated switches that open and close the circuits

• Resistor

• Switches

• Sound Modules

• Display Modules like Diode

• Circuits

• Basic Elements

• Gateway module

• Microcontroller

• Electric Modules

• Real-time Data Analysis

• Improved Scalability: can be deployed on the cloud

• Increased Accuracy: analyse the data

• Enhanced Security: identify threads -> measures