Differences

This shows you the differences between two versions of the page.

--- en:wyniki [2026/01/05 21:01] – hkordula
+++ en:wyniki [2026/01/21 15:16] (current) – hkordula
@@ Line 1: / Line 1: @@
-====== 3. Results and discussion ======
+===== Results and Discussion =====
-===== 3.1. Analysis of IoT system efficiency =====
+==== Analysis of IoT Systems Efficiency ====
-The main value of IoT is not connectivity itself, but data analytics. Traditional systems operate according to rigid schedules, whereas IoT allows for reactivity and proactivity. Data collected by sensors allows managers to dynamically reconfigure office space. The analysis of literature and market data shows that the implementation of IoT systems in building management (BMS) brings tangible benefits measurable in three main areas: energy, operational, and social.
+The primary value of IoT lies not in connectivity itself, but in data analytics. While traditional systems operate according to rigid schedules, IoT enables both reactivity and proactivity. The collected data allows managers to dynamically reconfigure spaces. Grieves and Vickers rightly point out that a fundamental assumption of modern digital systems is treating information as a substitute for wasted physical resources—time, energy, and materials. "However, the fundamental assumption of the Digital Twin model is that information replaces wasted physical resources, i.e., time, energy, and materials." (( Grieves Michael, Vickers John, Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems, New York 2017, p. 101. ))
-  * **Energy efficiency:** Buildings using advanced occupancy sensors and predictive algorithms show a decrease in electricity consumption of 15–30% annually. This results from eliminating "ghost runs" of HVAC and lighting systems in unoccupied zones.
+Based on the conducted literature review and market data analysis, the implementation of IoT systems in Building Management Systems (BMS) brings measurable benefits across three main areas: energy, operational, and social.
-  * **Optimization of maintenance costs:** Moving to a Predictive Maintenance model allows for extending the life cycle of technical equipment by approximately 20% and reducing the costs of sudden failures by nearly 40%.
-  * **Impact on productivity:** Analysis of IEQ (Indoor Environmental Quality) parameters indicates that precise control of CO2 concentration and lighting intensity translates into an increase in office work efficiency by 3–5%, which on a corporate scale is a gain that exceeds energy savings.
-===== 3.2. Discussion on implementation barriers =====
+<WRAP group>
+<WRAP half column>
+  * **Energy Efficiency:** Buildings utilizing advanced occupancy sensors and predictive algorithms show a decrease in electricity consumption by 15–30% annually. This results from eliminating "ghost loads" in HVAC and lighting systems in unoccupied zones.
+  * **Maintenance Cost Optimization:** Transitioning to a Predictive Maintenance model allows for extending the lifecycle of technical equipment by approximately 20% and reducing the costs of emergency failures by nearly 40%.
+  * **Impact on Productivity:** Analysis of IEQ (Indoor Environmental Quality) parameters indicates that precise control of CO2 concentration and lighting intensity translates into a 3–5% increase in office work efficiency, which, for large corporations, represents a gain exceeding energy savings.
+</WRAP>
-Despite numerous advantages, discussions in the scientific community point to two critical problems: cybersecurity and fragmentation of standards. Most IoT devices in buildings have limited computing power, which prevents the use of strong encryption. This makes a smart building a potential entry point for Ransomware attacks on the corporate network. Furthermore, the lack of full interoperability between manufacturers (e.g., difficulty connecting sensors from company X to the control unit from company Y) still forces investors into vendor lock-in.
+<WRAP half column>
+<gchart pie 300x300 "Share of Savings via IoT" center>
+Energy Efficiency = 30
+Maintenance Costs = 40
+Productivity and IEQ = 20
+Other = 10
+</gchart>
+</WRAP>
+</WRAP>
-===== 3.3. Author's example: Design of an air quality monitoring system in a conference room =====
+==== Discussion on Implementation Barriers ====
-As part of the practical analysis, a simple measuring node dedicated to monitoring environmental parameters in a conference room was designed and tested.
+Despite numerous advantages, scholarly discussion highlights two critical issues: cybersecurity and standard fragmentation. Most IoT devices in buildings have limited computing power, which prevents the use of strong encryption. This makes a smart building a potential entry point for Ransomware attacks on the corporate network. Furthermore, the lack of full interoperability between manufacturers (e.g., the difficulty of connecting sensors from Company X to a control unit from Company Y) still forces investors into vendor lock-in. Moreover, Al-Fuqaha notes that in such heterogeneous networks, the secure distribution of cryptographic keys among billions of devices from different manufacturers remains an open research problem that has not yet been fully standardized. "One of the open problems in IoT security that has not been addressed in standards is the distribution of keys among devices." (( Al-Fuqaha Ala, Guizani Mohsen, Mohammadi Mehdi, Aledhari Mohammed, Ayyash Moussa, Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications, New York 2015, p. 2364. ))
-**Concept and components**
+<WRAP center 60%>
+^ Strengths (S) ^ Weaknesses (W) ^
-The system is based on Edge Computing architecture – this means that the decision to activate support systems (e.g., ventilation) is made directly on the device, which shortens response time and increases building reliability in the event of a Wi-Fi network failure.
+| OPEX reduction by 15-30% | High initial cost (CAPEX) |
+| Predictive maintenance | Low computing power of devices |
-**Tools used:**
+^ Opportunities (O) ^ Threats (T) ^
-  * **Simulator:** Wokwi (environment for prototyping embedded systems).
+| Digital Twin technology | Cyberattacks and Ransomware |
-  * **Microcontroller:** ESP32 (chosen for its low power consumption).
+| Productivity increase by 3-5% | Lack of standardization (Vendor lock-in) |
-  * **Sensor:** DHT22 (digital sensor measuring temperature and humidity with high precision).
+</WRAP>
-  * **Indicator:** LED with a 220Ω resistor (simulating the activation of air conditioning/heat recovery).
-**Connection diagram (Hardware)**
-The following diagram shows the physical structure of connections made on a virtual microcontroller:
-{{ :wiki:schemat.png?nolink&600 |}}
-**DHT22:**
-  * VCC -> 3.3V
-  * GND -> GND
-  * Data -> GPIO 15
-**LED:**
-  * Anode (+) -> resistor -> GPIO 2
-  * Cathode (-) -> GND
-{{ :wiki:schemat_polaczen.svg |ESP32 connection diagram}}
-**Software implementation**
-The following code performs readings every 2 seconds. It uses an error-filtering function (isnan) and threshold logic for comfort temperature set between 18°C and 24°C and humidity between 30% and 60%.
-{{ :wiki:kod.png?nolink&600 |}}
-**Result:** Analysis of the prototype showed that the DHT22 sensor, despite its simplicity, is sufficient for monitoring general work comfort in a conference room. In a real-world implementation in an IoT-managed building, the code should include a Wi-Fi section responsible for sending this data to a central database, allowing the property manager to generate daily reports and optimize the building's heating curve.
-{{ :wiki:logi.png?nolink&600 |}} {{ :wiki:dioda.png?nolink&600 |}}