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--- en:iot-reloaded:iot_systems_architectures [2025/01/06 20:48] – pczekalski
+++ en:iot-reloaded:iot_systems_architectures [2025/05/13 14:45] (current) – pczekalski
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-===== IoT v.s. Wireless Sensor Networks (WSNs) =====
+===== IoT vs Wireless Sensor Networks (WSNs) =====
 People often think of IoT systems as WSN systems (figure {{ref>Typical_WSN_architecture}}), which is usually a close but inaccurate concept. There are several distinctive features of WSNs among other systems:
   - **Wireless:** Item Sensors that observe (sense) their surroundings and transmit data to the data concentration hub are sensor devices, sometimes called Nodes or Motes.
   - **Self-configuration Typically:** WSNs can self-configure due to their open architecture, hoping communication is based on Node-to-Node communication with dynamic pathfinding. So, they are capable of infrastructure-less deployment.
-  - **Limited resources:** Power consumption and size limit computing power and memory available. Therefore, typically, WSNs provide values through a synergy of multiple simple measurements instead of a single and more complex one like a hyperspectral camera or MIMO radar.
+  - **Limited resources:** Power consumption and size limit the computing power and memory available. Therefore, typically, WSNs provide values through a synergy of multiple simple measurements instead of a single and more complex one, like a hyperspectral camera or MIMO radar.
 <figure Typical_WSN_architecture>
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 ===== Difference Between WSN and IoT Systems =====
-Due to developments in infrastructure and communications technologies, IoT has grown far beyond simple interconnected devices as it is with WSN. While the IoT system might include WSN as its part, the IoT system functionality and application goal shift more towards decision-making and deeper data analysis. Because of its growing processing power, the availability of global wireless infrastructure, and synergies, IoT can solve complex tasks and support complex decisions.
+Due to developments in infrastructure and communications technologies, IoT has grown far beyond simple interconnected devices, as with WSN. While the IoT system might include WSN as part, the IoT system functionality and application goal shift more towards decision-making and deeper data analysis. Because of its growing processing power, the availability of global wireless infrastructure, and synergies, IoT can solve complex tasks and support complex decisions.
 **WSN v.s. IoT challenges:**
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 === Fog Computing ===
-Fog computing (figure {{ref>fog}})addressed the bottlenecks of cloud computing regarding data transport while providing the needed services to IoT systems.
+Fog computing (figure {{ref>fog}}) addresses the bottlenecks of cloud computing regarding data transport while providing the needed services to IoT systems.
 Fog computing is a trend that aims to process data near the source. It pushes applications, services, data, computing power, and decision-making away from the centralised nodes to the logical extremes of a network. Fog computing significantly decreases the data volume that must be moved between end devices and the cloud.
 Fog computing enables data analytics and knowledge generation closer to the data source. Furthermore, the dense geographic distribution of fog helps to attain a better-localised accuracy for many applications than the cloud processing of the data ((Arslan Munir, IFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for the Future Internet of Things, IEEE Consumer Electronics Magazine, Vol. 6, Issue 3, July 2017 )).\\
-The recent development of energy-efficient hardware with AI acceleration enters the fog class of the devices, putting fog computing in the middle of the interest of IoT application development and extending new horizons to them. Fog computing is more energy efficient than raw data transfer to the cloud and back, and in the current scale of the IoT devices, the application is meant for the future of the planet Earth. Fog computing usually also has a positive impact on IoT security, e.g., sending pre-processed and depersonalised data to the cloud and providing distributed computing capabilities that are more attack-resistant.
+The recent development of energy-efficient hardware with AI acceleration enters the fog class of devices, putting fog computing in the middle of the interest of IoT application development and extending new horizons to them. Fog computing is more energy efficient than raw data transfer to the cloud and back, and on the current scale of IoT devices, the application is meant for the future of the planet Earth. Fog computing usually also has a positive impact on IoT security, e.g., sending pre-processed and depersonalised data to the cloud and providing distributed computing capabilities that are more attack-resistant.
 <figure fog>
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 === Edge Computing ===
-Recent developments in hardware, power efficiency, and a better understanding of IoT data nature, including privacy and security, led to solutions where data is processed and pre-processed right to their source in the Edge class devices. Edge data processing on end-node IoT devices is crucial in systems where privacy is essential and sensitive data is not to be sent over the network (e.g. biometric data in a raw form). Moreover, distributed data processing can be considered more energy efficient in some scenarios where, e.g. extensive, power-consuming processing can be performed during green energy availability (figure {{ref>edge}}).
+Recent developments in hardware, power efficiency, and a better understanding of IoT data nature, including privacy and security, led to solutions where data is processed and pre-processed right at its source in the Edge class devices. Edge data processing on end-node IoT devices is crucial in systems where privacy is essential and sensitive data is not to be sent over the network (e.g. biometric data in a raw form). Moreover, distributed data processing can be considered more energy efficient in some scenarios where, e.g. extensive, power-consuming processing can be performed during green energy availability (figure {{ref>edge}}).
 <figure edge>
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   * **understanding** – in the case of IoT, it means systems' capability to process a significant amount of structured and unstructured data, extract the meaning of the data – produce a model that binds data to reality,
   * **reasoning** – involves decision-making according to the understood model and acquired data,
-  * **learning** – creating new knowledge from the existing, sensed data and elaborated models.
+  * **learning** – creating new knowledge from existing, sensed data and elaborated models.
 Usually, cognitive IoT systems or C-IoT are expected to add more resilience to the solution. Resilience is a complex term explained differently in different contexts; however, there are standard features for all resilient systems. As a part of their resilience, C-IoT should be capable of self-failure detection and self-healing that minimises or gradually degrades the system's overall performance. In this respect, the non-resilient system fails or degrades in a step-wise manner. In case of security issues, that system should be able to change its security keys and encryption algorithms and take other measures to cope with the detected threats. Self-optimisation abilities are often considered part of the C-IoT feature list to provide more robust solutions.
 Recent developments in the Fog and Edge class devices and the efficient software leverage cognitive IoT Systems to a new level.

en/iot-reloaded/iot_systems_architectures.1736196498.txt.gz · Last modified: 2025/01/06 20:48 by pczekalski