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Challenges, Innovations and Future Perspectives for Next-Generation Smart Buildings
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Challenges, Innovations and Future Perspectives for Next-Generation Smart Buildings

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 January 2026) | Viewed by 2953

Special Issue Editors

Prof. Dr. Christos-Nikolaos Anagnostopoulos

E-Mail Website
Guest Editor
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Interests: 3D modelling; 3D visualization; digital culture; face recognition; mixed reality; multimedia
Special Issues, Collections and Topics in MDPI journals
Dr. Asimina Dimara

E-Mail Website
Guest Editor
Department of Cultural Technology and Communication Department, University of the Aegean, 81100 Mytilene, Greece
Interests: legacy buildings degradation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of smart buildings has revolutionized how we design, construct, and interact with built environments. These next-generation structures integrate advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and edge computing to enhance efficiency, sustainability, and occupant well-being. However, the rapid advancements in technology come with a plethora of challenges, including interoperability, cybersecurity, energy management, and scalability. Moreover, the need to address global concerns such as climate change and urbanization underscores the importance of developing resilient and adaptive smart building solutions. This Special Issue steps into the critical issues shaping the future of smart buildings. It highlights emerging trends, innovative approaches, and the pressing challenges faced by stakeholders in designing and managing these advanced systems. By addressing these themes, this issue aims to inspire innovative solutions and foster advancements that contribute to smarter, more sustainable, and human-centered environments. We welcome diverse perspectives that explore the transformative potential of smart buildings in meeting the demands of a rapidly changing world.

Sensors play a pivotal role in monitoring and controlling environmental parameters, such as temperature, humidity, air quality, and lighting, to optimize building performance and occupant comfort. Advanced sensor systems enable real-time data acquisition, facilitating the integration of Internet of Things (IoT) and artificial intelligence (AI) to enhance energy efficiency, safety, and automation in smart buildings. Furthermore, as smart buildings evolve, novel sensing technologies are required to address challenges such as multi-modal data fusion, interoperability, and cybersecurity. Applications such as predictive maintenance, occupancy detection, and health monitoring for occupants also rely on innovative sensor systems. These advancements directly align with the journal’s focus on sensor development, deployment, and their impact on technology-driven solutions. By addressing the critical challenges and future trends in smart building design and management, this topic contributes to the ongoing exploration of how sensors can transform modern living and ensure sustainable and adaptive built environments.

Prof. Dr. Christos-Nikolaos Anagnostopoulos
Dr. Asimina Dimara
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart buildings
  • next-generation technologies
  • Internet of Things
  • artificial intelligence
  • energy management
  • sustainability
  • cybersecurity
  • urbanization
  • building automation
  • human-centered design

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Published Papers (2 papers)

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Research

24 pages, 6456 KB  
Article
Measurement-Based Modeling of Large-Scale and Time-Varying Small-Scale Fading for LoRa in Indoor Multi-Floor Environments
by Gabriel Nascimento Lira, Danilo Brito Teixeira de Almeida, Daniel da Silva Sarmento, João Victor Gadelha Cavalcante Ciraulo, Fabricio Braga Soares de Carvalho and Waslon Terllizzie Araújo Lopes
Sensors 2026, 26(4), 1152; https://doi.org/10.3390/s26041152 - 10 Feb 2026
Viewed by 727
Abstract
The deployment of robust Internet of Things (IoT) networks within smart buildings requires a thorough understanding of radio propagation in complex indoor environments. Long Range (LoRa) technology is a promising solution for such applications due to its long range and low power consumption. [...] Read more.
The deployment of robust Internet of Things (IoT) networks within smart buildings requires a thorough understanding of radio propagation in complex indoor environments. Long Range (LoRa) technology is a promising solution for such applications due to its long range and low power consumption. However, its performance in multi-floor structures is heavily influenced by site-specific propagation conditions. This paper presents an empirical characterization of LoRa signal propagation at 433 MHz within a four-story university building. Extensive measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) were conducted to model both large-scale and small-scale fading effects. A log-distance path loss model with a Floor Attenuation Factor (FAF) was derived, yielding a path loss exponent of n=2.53, an FAF of 5.52 dB per floor, and a log-normal shadowing standard deviation of σ=6.93 dB. Time-varying small-scale fading was successfully characterized by a Markov-modulated process (Markov Small-Scale Fading). Furthermore, a non-linear relationship between RSSI and SNR was identified and modeled using a four-parameter logistic function, revealing a dynamic range of approximately 30 dB for the transceivers and a minimum measurable RSSI of −125 dBm. The results validate the proposed models and demonstrate that LoRa can provide reliable, building-wide wireless sensor coverage, offering essential guidelines for the planning and deployment of indoor IoT infrastructure in multi-floor environments. Full article
(This article belongs to the Special Issue Challenges, Innovations and Future Perspectives for Next-Generation Smart Buildings)
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13 pages, 5109 KB  
Article
Method for Generating Real-Time Indoor Detailed Illuminance Maps Based on Deep Learning with a Single Sensor
by Seung-Taek Oh, You-Bin Lee and Jae-Hyun Lim
Sensors 2025, 25(16), 5154; https://doi.org/10.3390/s25165154 - 19 Aug 2025
Cited by 1 | Viewed by 1579
Abstract
Emerging lighting technology aims to enhance indoor light quality while conserving energy through control systems that integrate with natural light. In related technologies, it is crucial to identify quickly and accurately indoor light environments that are constantly changing due to natural light. Consequently, [...] Read more.
Emerging lighting technology aims to enhance indoor light quality while conserving energy through control systems that integrate with natural light. In related technologies, it is crucial to identify quickly and accurately indoor light environments that are constantly changing due to natural light. Consequently, a large number of sensors must be installed, but installing multiple sensors would cause an increasing data processing load and inconvenience to users’ activities. Some have attempted to calculate natural light characteristics, such as solar radiation and color temperature cycles, and implement natural light lighting technology by applying deep learning technology. However, there are only a few cases of using deep learning to analyze indoor illuminance, which is essential for commercializing natural light lighting technology. Research on minimizing the number of sensors is also lacking. This paper proposes a method for generating a detailed indoor illuminance map using deep learning, which calculates the illuminance values of the entire indoor area with a single illuminance sensor. A dataset was constructed by collecting dynamically changing indoor illuminance and the position of the sun, and a single sensor was selected through analysis. Then, a DNN model was built to calculate the illuminance of every region of an indoor space by inputting the illuminance measured by a single sensor and the position of the sun, and it was applied to generate a detailed indoor illuminance map. Research has demonstrated that calculating the illuminance levels across an entire indoor area is feasible. Specifically, on clear days with a color temperature anomaly of about 1%, a detailed illuminance map of the indoor space was created, achieving an average MAE of 2.0 Lux or an MAPE of 2.5%. Full article
(This article belongs to the Special Issue Challenges, Innovations and Future Perspectives for Next-Generation Smart Buildings)
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