# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 5 Data Aggregation Overview
### Data Fusion  
Building management systems are evolving to integrate data from various IoT-based sensors. This provides information on the core operational systems within a facility, allowing facility managers to optimize building performance. The goal of gathering relevant and targeted information refers to the term data fusion. [PERSON] and [PERSON] [58] describe data fusion as a collection of methods for integrating data from various sensors and related information from associated databases, aiming to achieve greater accuracy and more precise inferences than using a single sensor alone. Dueto the extensive body of literature on data fusion, this section does not aim to provide a comprehensive review on the field. Instead, it aims to delineate the key phases inherent in the sensor fusion framework. In the context of smart buildings, sensor fusion involves combining data from various IoT sensors to create a cohesive, accurate, and dependable understanding of the monitored system's behavior, thereby improving building operations. The main considerations are how to obtain data, how to standardize the data, and how to analyze the integrated data. Figure 4 presents a sensor fusion framework delineated into four sequential phases. The initial \"Sense\" step encompasses the acquisition of environmental data from the physical world, utilizing a network of various IoT sensors and actuators to capture designated parameters such as temperature, humidity, pressure, occupancy, lighting, etc. After data acquisition, the \"Perceive\" stage is tasked with the interpretation of sensory information, converting raw data into coherent insights that can be leveraged for analytical purposes. The most common approach at this phase is analyzing the correlations between sensors' activation and action recognition. The third \"Plan\" phase involves strategic analysis of the interpreted data to formulate actionable plans. Within the smart building domain, this is typically aimed at optimizing energy consumption, scheduling preventive maintenance, or modulating environmental conditions to enhance occupant comfort. Finally, the \"Act\" phase executes the strategic plan, directly interfacing with the BACS to implement the determined actions (e.g., modulating HVAC settings or energy consumption). [PERSON] and [PERSON] [59] classify data fusion into two categories based on the type of architecture: centralized and decentralized data fusion. In centralized fusion, data from various sensors are sent to a central location, which is responsible for making the final decision. The central processor acts as the fusion node, aggregating raw measurements from all input sources. The primary advantage of this architecture is a unified processing environment, which contributes to more consistent and comprehensive data analysis. However, sending raw data through the network requires high bandwidth [60]. Another critical issue is the variable time delays in transferring information between sources, which affect the fusion results [61]. Conversely, in decentralized fusion, each sensor makes its own local decision and then sends it to the fusion node for further processing. [PERSON] [61] describes a decentralized architecture as a network of nodes, each with its own processing capabilities, without a single central point for data fusion. Each node independently combines its local information with data received from its peers. The primary drawback of this architecture is scalability, which becomes challenging as the number of nodes increases. However, this issue can be alleviated through the use of DevOps practices [62] and IoT-edge-cloud continuum frameworks [63; 64]. Different kinds of data fusion techniques are depicted in Figure 5.  
* Data association is the process of determining which measurements (or observations) correspond to each specific target being tracked.