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Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

Digital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.

The authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.

While not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.

The proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.

This article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.

The paper aims to present an innovative method for imaging the pressure distribution between two interface surfaces. The physical principles behind the design of the pressure imaging system are explained, and some case studies involving the use of this technology in diverse applications are described.

The XSENSOR pressure sensor is comprised of a matrix of capacitive sensing elements. Pressure applied to the surface of the sensing element causes a change in capacitance that is correlated to a change in pressure. Proprietary Windows based software compensates for sensor non‐linearity, hysteresis, and creep over time, resulting in enhanced accuracy.

XSENSOR's capacitive based pressure imaging sensors can graphically display pressure distributions in real time between virtually any two surfaces in contact. The sensor element is accurate, thin, flexible, and robust. These physical characteristics minimize any artificial influences created by the presence of the sensor during data collection.

Pressure imaging technology can be used in industrial and engineering environments for product design and verification, process control, or quality assurance.

This paper will be useful to the engineer or business manager interested in applying sensor technology to solve engineering or design problems.

The organizational digital transformation (ODT) in companies presents small and medium-sized enterprises (SMEs) – who remain at the beginning of this transformation – with the challenge of offering digital services based on sensor technologies. Against this backdrop, the present paper identifies ways SMEs can enable digital servitization through sensor technology and defines the possible scope of the organizational transformation process.

Around 21 semi-structured interviews were conducted with experts from different hierarchical levels across the German manufacturing SME ecosystem. Using the Gioia methodology, fields of action were identified by focusing on influencing factors and opportunities for developing these digital services to offer them successfully in the future.

The complexity of existing sensor offerings must be mastered, and employees' (data) understanding of the technology has increased. Knowledge gaps, which mainly relate to technical and organizational capabilities, must be overcome. The potential of sensor technology was considered on an individual, technical and organizational level. To enable the successful implementation of service offerings based on sensor technology, all relevant stakeholders in the ecosystem must network to facilitate shared value creation. This requires standardized technical and procedural adaptations and is an essential prerequisite for data mining.

Based on this study, current problem areas were analyzed, and potentials that create opportunities for offering digital sensor services to manufacturing SMEs were identified. The identified influencing factors form a conceptual framework that supports SMEs' future development of such services in a structured manner.

Construction safety has been a long-term problem in the development of the construction industry. An increasing number of smart construction sites have been designed using different techniques to reduce injuries caused by construction accidents and achieve proactive risk control. However, comprehensive smart construction site safety management solutions and applications have yet to be developed. Thus, this study proposes a smart construction site framework for safety management.

A safety management system based on a cyber-physical system is proposed. The system establishes risk data synchronization mapping between the virtual construction and physical construction sites through scene reconstruction design, data awareness, data communication and data processing modules. Personnel, mechanical and other risks on site will be warned and controlled.

The results of the case study have proved the management benefits of the system. On-site workers gradually realized that they should enter the construction site based on the standard process. And the number of people close to the construction hazard areas decreased.

There are some limitations in the technology of smart construction site. The modeling speed can be faster, the data collection can be timelier, and the identification of unsafe behavior can be integrated into the system. Construction quality and efficiency issues in a virtual construction site will also be solved in further research.

In this paper, this system is actually applied in the mega project management process. More practical projects can use the management ideas and method of this paper to ensure on-site safety.

This study is among the first attempts to build a complete smart construction site based on CPS and apply it in practice. Personnel, mechanical, components, environment information will be displayed on the virtual construction site. It will greatly promote the development of the intellectualized construction industry in the future.

A smart city integrates a comprehensive suite of technologies, which inherently require data to function effectively. It is designed with the intention of amassing all available data concerning machines, devices, infrastructure, individuals and their surroundings. This commentary addresses the evolution of smart cities over time, the increasing extent of data collection, the growing pressure on personal privacy and people's reactions to these trends. The article highlights the contradiction between the needs of the city and the desires of its inhabitants. It notes that people react differently to gradual versus abrupt changes in data-collecting technologies and services. It also suggests that more work needs to be done to prepare both smart cities and the human population for a sustainable, mutually beneficial future.

This commentary presents a viewpoint on the subject of data collection and privacy in smart cities, drawing on various sources to support its observations and conclusions.

The primary focus of this discussion is on the technological evolution of the cities. It emphasizes that, as cities get smarter, they offer more conveniences in exchange for various types of data, highlighting the likelihood that pressure on personal privacy will continue to escalate. This is due to the increasing pervasiveness of data-collecting technologies in every aspect of lives and urban environments. These environments are expected to become progressively smarter each year. Given this context, and to ensure a seamless transition to smart and sustainable cities, it is imperative that today's privacy discussions start to focus not only just on the existing but also on the future conditions and challenges that citizens are expected to encounter.

This commentary delves into the existing gaps in understanding the contradiction between the data-collection “needs” of smart cities, the direction in which the cities are evolving and people's awareness of how much data they will have to surrender in the future. It also highlights the risk of people gradually relinquishing nearly all their privacy, often without noticing, in exchange for the ever-increasing conveniences offered by smart cities.

Wireless sensor network (WSN) and mobile crowd sensing (MCS) technologies face some challenges, especially when deployed in a large environment such as a smart city environment. WSN faces network latency, packets delivery and limited lifetime due to the nature of the used constrained internet of things small devices and low power network. On the other hand, most of the current applications that adapt MCS technology use 3G or long term evalution network to collect data and send them directly to the server. This leads to higher battery and bandwidth consumption and higher data cost.

This paper proposes a hybrid routing protocol based on the routing protocol (RPL) protocol that combines the two wireless sensing technologies (WSN and MCS) and allows the integration between them. The aim is to use MCS nodes in an opportunistic way to support static WSN nodes to enhance the performance.

The evaluation of the proposed protocol was conducted in a static WSN to study the impact of the integration on the WSN performance. The results reveal a good enhancement on packet delivery ratio (17% more), end-to-end delay (50% less) and power consumption (25% less) compared with native RPL (without MCS integration).

The authors believe that the hybrid-RPL protocol can be useful for sensing and data collection purposes, especially in urban areas and smart city contexts.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.