In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

Purpose: This paper reviews the state-of-the art research in life cycle assessment (LCA) applied to buildings. It focuses on current research trends, and elaborates on gaps and directions for future research. Methods: A systematic literature review was conducted to identify current research and applications of LCA in buildings. The proposed review methodology includes (i) identifying recent authoritative research publications using established search engines, (ii) screening and retaining relevant publications, and (iii) extracting relevant LCA applications for buildings and analyzing their underpinning research. Subsequently, several research gaps and limitations were identified, which have informed our proposed future research directions. Results and discussions: This paper argues that humans can attenuate and positively control the impact of their buildings on the environment, and as such mitigate the effects of climate change. This can be achieved by a new generation of LCA methods and tools that are model based and continuously learn from real-time data, while informing effective operation and management strategies of buildings and districts. Therefore, the consideration of the time dimension in product system modeling is becoming essential to understand the resulting pollutant emissions and resource consumption. This time dimension is currently missing in life cycle inventory databases. A further combination of life cycle impact assessment (LCIA) models using time-dependent characterization factors can lead to more comprehensive and reliable LCA results. Conclusions and recommendations: This paper promotes the concept of semantic-based dynamic (real-time) LCA, which addresses temporal and spatial variations in the local built and environmental ecosystem, and thus more effectively promotes a “cradle-to-grave-to-reincarnation” environmental sustainability capability. Furthermore, it is critical to leverage digital building resources (e.g., connected objects, semantic models, and artificial intelligence) to deliver accurate and reliable environmental assessments.

Purpose: Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods: In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results: The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions: The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

Handoff mechanisms allow mobile users to move across multiple wireless access points while maintaining their voice and/or data sessions. A traditional handoff process is concerned with smoothly transferring a mobile device session from its current access point (or cell) to a target access point (or cell). These handoff characteristics are sufficient for voice calls and background data transfers, however nowadays many mobile applications are heavily based on data and processing capabilities from the cloud. Such applications, especially those that require greater interactivity, often demand not only a smooth session transfer, but also the maintenance of quality of service requirements that impact a user's experience. In this context, the Fog Computing paradigm arises to overcome delays encountered when applications need low latency to access data or offload processing to the cloud. Fog computing introduces a distributed cloud layer, composed of cloudlets (i.e., "small clouds" with lower computational capacity), between the user and the cloud. Cloudlets allow low latency access to data or processing capabilities, which can be accomplished by offering a VM to the user. An overview of Fog computing is first providing, relating it to general concepts in Cloud-based systems, followed by a general architecture to support virtual machine migration in this emerging paradigm - discussing both the benefits and challenges associated with such migration.