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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.

The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment.

The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws.

The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws.

Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas.

The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems.

In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

To mitigate the problems in sensor-based facility management (FM) such as lack of detailed visual information about a built facility and the maintenance of large scale sensor deployments, an integrated data source for the facility’s life cycle should be used. Building information modeling (BIM) provides a useful visual model and database that can be used as a repository for all data captured or made during the facility’s life cycle. It can be used for modeling the sensing-based system for data collection, serving as a source of all information for smart objects such as the sensors used for that purpose. Although few studies have been conducted in integrating BIM with sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between FMs and Internet of Things (IoT) companies in cases encountered failed sensors has received the least attention in the technical literature. Therefore, the purpose of this paper is to conceptualize and develop a BIM-based system architecture for fault detection and alert generation for malfunctioning FM sensors in smart IoT environments during the operational phase of a building to ensure minimal disruption to monitoring services.

This paper describes an attempt to examine the applicability of BIM for an efficient sensor failure management system in smart IoT environments during the operational phase of a building. For this purpose, a seven-story office building with four typical types of FM-related sensors with all associated parameters was modeled in a commercial BIM platform. An integrated workflow was developed in Dynamo, a visual programming tool, to integrate the associated sensors maintenance-related information to a cloud-based tool to provide a fast and efficient communication platform between the building facility manager and IoT companies for intelligent sensor management.

The information within BIM allows better and more effective decision-making for building facility managers. Integrating building and sensors information within BIM to a cloud-based system can facilitate better communication between the building facility manager and IoT company for an effective IoT system maintenance. Using a developed integrated workflow (including three specifically designed modules) in Dynamo, a visual programming tool, the system was able to automatically extract and send all essential information such as the type of failed sensors as well as their model and location to IoT companies in the event of sensor failure using a cloud database that is effective for the timely maintenance and replacement of sensors. The system developed in this study was implemented, and its capabilities were illustrated through a case study. The use of the developed system can help facility managers in taking timely actions in the event of any sensor failure and/or malfunction to ensure minimal disruption to monitoring services.

However, there are some limitations in this work which are as follows: while the present study demonstrates the feasibility of using BIM in the maintenance planning of monitoring systems in the building, the developed workflow can be expanded by integrating some type of sensors like an occupancy sensor to the developed workflow to automatically record and identify the number of occupants (visitors) to prioritize the maintenance work; and the developed workflow can be integrated with the sensors’ data and some machine learning techniques to automatically identify the sensors’ malfunction and update the BIM model accordingly.

Transferring the related information such as the room location, occupancy status, number of occupants, type and model of the sensor, sensor ID and required action from the BIM model to the cloud would be extremely helpful to the IoT companies to actually visualize workspaces in advance, and to plan for timely and effective decision-making without any physical inspection, and to support maintenance planning decisions, such as prioritizing maintenance works by considering different factors such as the importance of spaces and number of occupancies. The developed framework is also beneficial for preventive maintenance works. The system can be set up according to the maintenance and time-based expiration schedules, automatically sharing alerts with FMs and IoT maintenance contractors in advance about the IoT parts replacement. For effective predictive maintenance planning, machine learning techniques can be integrated into the developed workflow to efficiently predict the future condition of individual IoT components such as data loggers and sensors, etc. as well as MEP components.

Lack of detailed visual information about a built facility can be a reason behind the inefficient management of a facility. Detecting and repairing failed sensors at the earliest possible time is critical to ensure the functional continuity of the monitoring systems. On the other hand, the maintenance of large-scale sensor deployments becomes a significant challenge. Despite its importance, few studies have been conducted in integrating BIM with a sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between facility managers and IoT companies in cases encountered failed sensors. In this paper, a cloud-based BIM platform was developed for the maintenance and timely replacement of sensors which are critical to ensure minimal disruption to monitoring services in sensor-based FM.

The purpose of this study is to investigate the effect of internal and external factors on the accuracy and consistency of the data provided by mobile-embedded micro-electromechanical system (MEMS) pressure sensors based on smartphones currently in use.

For this purpose, sensor type and smartphone model have been regarded as internal factors, whereas temperature, location and usage habits have been considered as external factors. These factors have been investigated by examining data sets provided by sensors from 14 different smartphones. In this context, internal factors have been analyzed by implementing accuracy assessment processes for three different smartphone models, whereas external factors have been evaluated by analyzing the line charts which present timely pressure changes.

The study outlined that the sensor data at different sources have different characteristics due to the affecting parameters. Even if the pressure sensors are used under similar circumstances, data of these sensors have inconsistencies because of the sensor drift originated by internal factors. This study concluded that it was not applicable to provide a common correction coefficient for pressure sensor data of each smartphone model. Therefore, relative data (pressure differences) should be taken into consideration rather than absolute data (pressure values) when developing mobile applications using sensor data.

Results of this study can be used as the guideline for developing mobile applications using MEMS pressure sensors. One of the main finding of this paper is promoting the use of relative data (pressure differences) rather than absolute data (pressure values) when developing mobile applications using smartphone-embedded sensor data. This significant result was proved by examinations applied with in the study and can be applied by future research studies.

Existing studies mostly evaluate the use of MEMS pressure sensor data obtained from limited number of smartphone models. As each smartphone model has a specific technology, factors affecting the sensor performances should be identified and analyzed precisely in terms of smartphone models for providing extensive results. In this study, five smartphone models were used fractionally. In this context, they were used for examining the common effects of the factors, and detailed accuracy assessments were applied by using two high-tech smartphones in the market.

The purpose of this paper is to present a novel and simple prediction model of long-term metal oxide semiconductor (MOS) gas sensor baseline, and it brings some new perspectives for sensor drift. MOS gas sensors, which play a very important role in electronic nose (e-nose), constantly change with the fluctuation of environmental temperature and humidity (i.e. drift). Therefore, it is very meaningful to realize the long-term time series estimation of sensor signal for drift compensation.

In the proposed sensor baseline drift prediction model, auto-regressive moving average (ARMA) and Kalman filter models are used. The basic idea is to build the ARMA and Kalman models on the short-term sensor signal collected in a short period (one month) by an e-nose and aim at realizing the long-term time series prediction in a year using the obtained model.

Experimental results demonstrate that the proposed approach based on ARMA and Kalman filter is very effective in time series prediction of sensor baseline signal in e-nose.

Though ARMA and Kalman filter are well-known models in signal processing, this paper, at the first time, brings a new perspective for sensor drift prediction problem based on the two typical models.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

Welding process is a complicated process influenced by many interference factors, a single sensor cannot get information describing welding process roundly. This paper simultaneously uses different sensors to get different information about the welding process, and uses multi‐sensor information fusion technology to fuse the different information. By using multi‐sensors, this paper aims to describe the welding process more precisely.

Electronic and welding pool image information are, respectively, obtained by arc sensor and image sensor, then electronic signal processing and image processing algorithms are used to extract the features of the signals, the features are then fused by neural network to predict the backside width of weld pool.

Comparative experiments show that the multi‐sensor fusion technology can predict the weld pool backside width more precisely.

The multi‐sensor fusion technology is used to fuse the different information obtained by different sensors in a gas tungsten arc welding process. This method gives a new approach to obtaining information and describing the welding process.

The purpose of this paper is to study the heat transfer effect of copper sensor and skin simulant on skin.

For the sensor, the physical and mathematical models of the thermal sensors were used to obtain the definite conditions in the heat transfer process of the sensor, and the heat transfer models of the two sensors were developed and solved respectively by using ANSYS WORKBENCH 19.0 software. The simulation results were compared with the experimental test results. For the skin, the numerical model of the skin model was developed and calculated. Finally, the heat transfer simulation performance of the two sensors was analyzed.

It is concluded that the copper sensor is more stable than the skin simulant, but the material and structure of the skin simulant is more suitable for skin simulation. The skin simulant better simulates the skin heat transfer. For all the factors in the model, the thermal properties of the material and the heat flux level are the key factors. The convective heat transfer coefficient, radiation heat transfer rate and the initial temperature have little influence on the results, which can be ignored.

The results show that there are still some differences between the experimental and numerical simulation values of the skin simulant. In the future, the thermal parameters of skin simulant and the influence of the thermocouple adhesion should be further examined during the calibration process.

The results suggest that the skin simulant needs to be further calibrated, especially for the thermal properties. The copper sensor on the flame manikin can be replaced by the skin simulant with higher accuracy, which will be helpful to improve the accuracy of performance evaluation of thermal protective clothing.

The application of computational fluid dynamics (CFD) technology can help to analyze the heat transfer simulation mechanism of thermal sensor, explore the influence of thermal performance of thermal sensor on skin simulation, provide basis for the development of thermal sensor and improve the application system of thermal sensor. Based on the current research status, this paper studies the internal heat transfer of the sensor through the numerical modeling of the copper sensor and skin simulant, so as to analyze the effect of the sensor simulating skin and the reasons for the difference.

In this paper, the sensor itself is numerically modeled and the heat transfer inside the sensor is studied.

An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.

This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.

The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.

The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

Within smart homes, ambient sensors are used to monitor interactions between users and the home environment. The data produced from the sensors are used as the basis for the inference of the users' behaviour information. Partitioning sensor data in response to individual instances of activity is critical for a smart home to be fully functional and to fulfil its roles, such as correctly measuring health status and detecting emergency situations. The purpose of this study is to propose a similarity‐based segmentation approach applied on time series sensor data in an effort to detect and recognise activities within a smart home.

The paper explores methods for analysing time‐related sensor activation events in an effort to undercover hidden activity events through the use of generic sensor modelling of activity based upon the general knowledge of the activities. Two similarity measures are proposed to compare a time series based sensor sequence and a generic sensor model of an activity. In addition, a framework is developed for automatically analysing sensor streams.

The results from evaluation of the proposed methodology on a publicly accessible reference dataset show that the proposed methods can detect and recognise multi‐category activities with satisfying accuracy, in addition to the capability of detecting interleaved activities.

The concepts introduced in this paper will improve automatic detection and recognition of daily living activities from timely ordered sensor events based on domain knowledge of the activities.