Integrated Sensor Technology Research Articles

Impersonation Attack Detection in Mobile Edge Computing by Levering SARSA Technique in Physical Layer Security

Smart health systems typically integrate sensor technology with the Internet of Things, enabling healthcare systems to monitor patients. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. However, the communication between the edge node and the mobile user is susceptible to impersonation attacks in mobile edge computing (MEC) for the biomedical application. For this purpose, we propose a detection mechanism for medical and healthcare services, i.e., reinforcement learning for impersonation attacks. We construct a system model of MEC, a key generation model (KGM), and an impersonation attack model (IAM). In addition, we also design an impersonation attack detection algorithm based on the SARSA technique under the IAM. In our proposed work, the SARSA-based method outplays the detection of impersonation attacks in the dynamic environment compared to the traditional Q-learning technique. Finally, we evaluate the false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER) in the hypothesis tests to compare the performance of our proposed method with the traditional Q-learning. In comparison to the classic Q-learning based technique, simulation experiments show that the suggested approach can avoid impersonation attacks in a dynamic environment for medical and healthcare services. The results also indicate that the SARSA technique has a high detection accuracy and low average error rate compared to the conventional Q-learning based approach.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Automated Surveillance of Lepidopteran Pests with Smart Optoelectronic Sensor Traps

Several lepidopterans are pests in horticulture and pose biosecurity risks to trading countries worldwide. Efficient species-specific semiochemical lures are available for some of these pests, facilitating the implementation of surveillance programmes via trapping networks. These networks have a long history of success in detecting incursions of invasive species; however, their reliance on manual trap inspections makes these surveillance programmes expensive to run. Novel smart traps integrating sensor technology are being developed to detect insects automatically but are so far limited to expensive camera-based sensors or optoelectronic sensors for fast-moving insects. Here, we present the development of an optoelectronic sensor adapted to a delta-type trap to record the low wing-beat frequencies of Lepidoptera, and remotely send real-time digital detection via wireless communication. These new smart traps, combined with machine-learning algorithms, can further facilitate diagnostics via species identification through biometrics. Our laboratory and field trials have shown that moths flying in/out of the trap can be detected automatically before visual trap catch, thus improving early detection. The deployment of smart sensor traps for biosecurity will significantly reduce the cost of labour by directing trap visits to the locations of insect detection, thereby supporting a sustainable and low-carbon surveillance system.

Treatment of cardiac resynchronization therapy non-responders: current approaches and new frontiers

ABSTRACT Introduction Cardiac resynchronization therapy (CRT) has developed into a very effective technology for patients with decreased systolic function and has substantially improved patients’ clinical course. However, non-responsiveness to CRT, described as lack of reverse cardiac chamber remodeling, leading to lack to improve symptoms, heart failure hospitalizations or mortality, is common, rather unpredictable, and not fully understood. Areas covered This article aims to discuss key factors that are impacting CRT response, from patient selection to LV lead position, to structured follow-up in CRT clinics. Secondly, common causes and interventions for CRT non-responsiveness are discussed. Next, insight is given into technologies representing new and feasible interventions as well as pacing strategies in this group of patients that remain challenging to treat. Finally, an outlook is given into future scientific development. Expert opinion Despite the progress that has been made, CRT non-response remains a significant and complex problem. Patient management in interdisciplinary teams including heart failure, imaging, and cardiac arrhythmia experts appears critical as complexity is increasing and CRT non-response often is a multifactorial problem. This will allow optimization of medical therapy, and the use of new integrated sensor technologies and telemedicine to ultimately optimize outcomes for all patients in need of CRT.

From patient generated data to precision medicine: An example of a patient-centered application in oncology.

e13559 Background: To practice precision medicine, the inclusion of patients in therapy is becoming increasingly important. Terms such as ‘patient empowerment’ (PE), ‘patient reported outcome’, and ‘patient generated data’ (PGD) are becoming quite present in current research. In order to implement PE consistently and integrate PGD in therapy, it is necessary to provide patients with specific tools. Methods: In order to develop such a corresponding system, challenges for implementation were identified as well as the current state of PGD in oncology. The aim was to design a system suitable for everyday use that allows the measurement of individual effects like quality of life (QoL) directly at the patient. Based on the positive experience of a previous developed questionnaire tool (EQU) to support physician-patient communication and the findings of the methodology a new patient-centered system was conceptualized with an automated data collection using sensor systems. A decisive factor was to support patients in self-management between treatment periods. This solution was then discussed extensively with stakeholders in order to identify and prioritize functionality and therefore incorporate specific user needs of oncological patients. Results: A great acceptance and need for tools to support patients especially between appointments was identified. In order to enable the recognition of individual characteristics, a continuous survey is essential. Based thereon an application called Lion-App for longitudinal QoL assessment in oncology was developed. A user-centered development aligns the app with the needs of the patients while HL7 FHIR provides the framework for an interoperable exchange. To reduce the burden of continuous interaction, sensors are used for data collection. With an interpretation of this data in the context of QoL, patients are able to visualize their personal course to gain an even better understanding of their disease. With the possibility to perform self-management between appointments, patients are lifted to another level of empowerment. In addition, the gained knowledge may then be used to focus on the effects of the disease in the next appointment and thus take a self-determined role in treatment. First evaluations of the app have been started. Conclusions: There are many ways to use PGD in the manner of precision medicine. The main factor is to create awareness among patients, collect data in an exchangeable way and involve end users early into development. A great opportunity to reduce user interactions and therefore enable long-term usage is the integration of sensor technologies. As a secondary use, algorithms can be extended via the expanded data base to become even more individualized to the patient. Thus, in the long run, such a system may help to further strengthen the cooperation between patient and physician and thus to enable a personalized, QoL-based treatment in oncology.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.

Integrating Sensor Technology in Disposable Body-Worn Absorbent Products: A Qualitative Study to Define User Profile, (Technical) Criteria, Conditions, and Potential Benefits.

The purpose of this study was to define the user profile, (technical) criteria, conditions, and potential benefits of the integration of sensor technology in disposable body-worn incontinence materials. Qualitative study using a framework method. The sample included residents with incontinence, nurses, and decision-makers in a selection of Flemish nursing homes (Belgium). Semistructured interviews were performed between June and August 2020. The interviews with nurses included open-ended questions focusing on the user profile, (technical) criteria, conditions, and potential benefits of the integration of sensor technology in disposable body-worn incontinence materials. The interviews with decision-makers were supplemented with questions about purchase cost and other economic criteria (such as reimbursement). Interviews with residents included questions about wearing comfort. Interviews were tape-recorded and transcribed verbatim. Data were analyzed using a framework method. The user profile was defined as (1) residents with cognitive impairment and (2) residents who are bedridden or are severely limited in mobility. The following (technical) criteria emerged from the analyses: a small, thin, and oval/circular sensor, an adaptable or in front attachment of the sensor on the absorbent product, a real-time indication of the absorbent product's saturation, leakage detection, liquid stool detection, the automatic recording of incontinence-related data, a durable sensor easy to disinfect, and receiving notifications on a wearable device. Conditions included a stable connection between the wearable device and the sensor, accurate measurements, user-friendly system, comprehensible training, affordability, and data protection. Potential benefits included workload reduction, increased comfort for residents and staff, more person-centered care, increased quality of care, less skin damage and economic (eg, less costs due to less excessive absorbent product changes), and/or environmental (e.g. less waste) gains. Study findings identified the user profile, (technical) criteria, conditions, and potential benefits of the integration of sensor technology in disposable body-worn incontinence materials. Respondents reported problems with changing routines and skepticism about the sensor's accuracy. Based on findings from this study, we recommend involving representatives of all relevant stakeholders in the design of sensor technology to ensure users' needs and increase the use of sensor technology. Comprehensive education is recommended to inform nurses, residents, and their family members on the importance and benefits of the technology and to aid overcoming barriers to use (skepticism, resistance to new technologies, and changing care routines). Study findings also indicate that the sensor technology cannot replace the existing voiding programs; rather it should be an addition to routine continence care.

Guest Editorial Special Issue on Smart Sensing for Agriculture

Seamless integration of sensor technologies with traditional agriculture has led to a revolutionary transformation in agriculture. This has motivated researchers to use sensor technology, GPS guidance, control systems, drones, and software services for ensuring optimal conditions for the crops and soil. For instance, unmanned surveillance on the agricultural field, automated soil and crop assessment, etc., assist the farmers by monitoring the soil conditions, environmental factors, and crop features. Smart sensing enables the objects to be sensed and controlled remotely using network model. Sensors help in collecting real-time data about the crops, soil, and surrounding conditions. The collected data can be used in managing the agricultural activities by performing predictive analysis using various machine learning and deep learning techniques. These advanced learning techniques mitigate the human errors by analyzing the large amount of sensory data and facilitate smart sensing in agriculture.

The Effect of Quality Indicators on Beliefs about Medicines Reuse: An Experimental Study.

Background: A number of studies have examined beliefs about medicines reuse. Although the practice is prohibited in UK community pharmacy, it does take place elsewhere in the world where it relies on visual checks of returned medicines as an indicator of their quality. One proposal is to integrate sensor technology onto medication packaging as a marker of their quality instead. Our aim was to gauge people’s beliefs about medicines reuse, in an experiment, with or without sensor technology and with or without the promise of visual checks completed by a pharmacist, as experimental conditions, should the practice be sanctioned in the UK in the future. Methods: A between participant study was designed with two independent factors testing the hypothesis that sensors and visual checks would increase pro-medicines-reuse beliefs. A questionnaire was used to measure medicines reuse beliefs and collect qualitative comments. Results: Eighty-one participants took part. Attitudes toward medication offered for reuse, participants’ perceived social pressure to accept the medication, and their intention to take part in medicines reuse all increased with the presence of sensors on packaging and with the promise of pharmacist visual checking, with the former causing a greater increase than the latter, and the combination of both making the greatest increase. People’s qualitative comments explained their concerns about medicines reuse, validating the findings. The use of sensors on medication packaging warrants further investigation if regulators are to consider approving medicines reuse in the UK.

Guest Editorial Advanced Sensing and Sensor Fusion for Intelligent Transportation Systems

The seamless integration of sensor technologies with the transportation infrastructure has leveraged the sensing and communication capabilities to achieve a sustainable intelligent transportation system through which safety, traffic control, and infotainment applications can benefit from multiple sensors deployed in different elements of the transportation system. Being one of the most active research fields in both academia and industry, sensor technologies enable various types of vehicular applications, such as autonomous driving, precise fleet management, intelligent traffic and congestion management, etc. A combination of low cost and reliable passive road sensors as well as traffic sensors (e.g., piezoelectric or magnetic micro-electro-mechanical system (MEMS) sensors) along with the IoT and fog computing provide an enormous potential for improving traffic efficiency, driving safety, and ride comfort.

Research on Data Release and Location Monitoring Technology of Sensor Network Based on Internet of Things

The Internet of Things is another information technology revolution and industrial wave after computer, Internet and mobile communication. It is becoming a key foundation and an important engine for the green, intelligent and sustainable development of economic society. The new networked intelligent production mode characterized by the integration innovation of the Internet of Things is shaping the core competitiveness of the future manufacturing industry. The application of sensor network data positioning and monitoring technology based on the Internet of Things in industry, power and other industries is a hot field for the development of the Internet of Things. Sensor network processing and industrial applications are becoming increasingly complex, and new features have appeared in the sensor network scale and infrastructure in these fields. Therefore, the Internet of Things perception data processing has become a research hotspot in the deep integration process between industry and the Internet of Things. This paper deeply analyzes and summarizes the characteristics of sensor network perception data under the new trend of the Internet of Things as well as the research on location monitoring technology, and makes in-depth exploration from the release and location monitoring of sensor network perception data of the Internet of Things. Sensor network technology integrated sensor technology, micro-electromechanical system technology, wireless communication technology, embedded computing technology and distributed information processing technology in one, with easy layout, easy control, low power consumption, flexible communication, low cost and other characteristics. Therefore, based on the release and location monitoring technologies of sensor network data based on the Internet of Things in different applications, this paper studies the corresponding networking technologies, energy management, data management and fusion methods. Standardization system in wireless sensor network low cost, and convenient data management needs, design the iot oriented middleware, and develops the software and hardware system, the application demonstration, the results show that the design of wireless sensor network based on iot data monitoring and positioning technology is better meet the application requirements, fine convenient integration of software and hardware, and standardized requirements and suitable for promotion.

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

Children, parents, older adults, and caregivers routinely use sensor technology as a source of health information and health monitoring. The purpose of this paper is to describe three exemplars of research that used a human-centered approach to engage participants in the development, design, and usability of interventions that integrate technology to promote health. The exemplars are based on current research studies that integrate sensor technology into pediatric, adult, and older adult populations living with a chronic health condition. Lessons learned and considerations for future studies are discussed. Nurses have successfully implemented interventions that use technology to improve health and detect, prevent, and manage diseases in children, families, individuals and communities. Nurses are key stakeholders to inform clinically relevant health monitoring that can support timely and personalized intervention and recommendations.

Commentary: Ecology momentary assessment as a tool for understanding dynamic patterns in child and adolescent health and development – reflections on Russell and Gajos (2020)

Ecological momentary assessment (EMA) has served as a methodological tool across subdisciplines in psychology, shedding light on family, personality, and affective processes, and physical and mental health. In their review, Russell and Gajos demonstrate how EMA can overcome several limitations of traditional methods in developmental psychopathology to answer questions about mental and behavioral health in youth. They also provide thoughtful future directions on integrating sensor technology, advancing modeling techniques for temporally dense data, and employing EMA for delivering interventions. This commentary similarly advocates for the use of EMA but extends Russell and Gajos's review by emphasizing EMA's utility for understanding and revealing dynamic changes and processes along micro timescales that have relevance for youth's health and development. We discuss how EMA can be especially fruitful for advancing theory and practice when administered in bursts and when combined with traditional assessments and longer-term longitudinal designs. Because such studies are resource-intensive, we also consider how extant EMA studies can be leveraged and subjected to meta- and mega-analytic techniques to inform theories and future EMA designs. We conclude that EMA is a promising tool for elucidating dynamic fluctuations in experiences and intra- and interpersonal processes that contribute to child and adolescent development and risk.

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called “basic-confrontation”, which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.

High-Power, Narrow-Linewidth, Miniaturized Silicon Photonic Tunable Laser With Accurate Frequency Control

We have demonstrated a hybrid-integrated silicon photonic (SiPh) tunable laser in a miniaturized package suitable for coherent modules in small form factors such as OSFP and QSFP-DD. Through the integration of an in-house-designed high-power semiconductor optical amplifier (SOA), the developed SiPh laser has achieved a record-high output power of 21.5 dBm across the C-band. Also, this laser exhibits narrow linewidths down to 60 kHz, SMSR larger than 50 dB, low relative intensity noise below −150 dB/Hz, and a broad tuning range of 65 nm. These performance parameters are preferable for 400 Gb/s and beyond coherent communications using advanced high-order modulation formats. Moreover, an on-chip integrated sensor technology was developed for accurate laser frequency control. We have achieved 1 GHz frequency stability of SiPh laser against SOA current changes or package temperature variations between 10 °C and 80 °C. We further show the feasibility of this developed SiPh laser for 64 Gbaud, 16/64 QAM coherent transmission by integrating with an in-house InP-PLC hybrid coherent optical subassembly (COSA).

Editorial

The world's population increases every year and resources are limited, consequently the humanity faces the need to reinvent the way of living. As population grows, it is expected that in the coming years, the majority of people will live in urban areas. Thus urgent need to have cities where the well-being of people and the sustainability of the environment can be guaranteed [1]. In the last decade, the integration of sensor technologies, mobile internet, and information technology, has allowed the development of countless systems seeking to achieve the aforementioned objectives for the cities of the future. A Smart City can be defined as one that is able to take advantage of its own data, produced during its daily operation, in order to generate new information that allows it to improve its management and be more sustainable, more competitive, and offer better quality of life, thanks to participation and collaboration of all agents involved [2].

A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks.

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.

Sensing comfort in bicycling in addition to travel data

Abstract Bicycling is a key component of sustainable mobility. This paper gives an insight into the importance of bicycle-friendliness, also known as “bikeability” and its qualitative components like perception and emotions of the riders. The research applies a multi-methodical framework on bicycling aiming to identify stressful events on a bicycle ride. The goal was to test new bio-physiological sensors (like EDA device, eye tracker etc.) to develop a methodology on how sensor technologies can be integrated in the data collection processes and to discuss their practicability. The experiences in this field can help to foster a long-term integration of sensor technology into travel survey methodologies and further into transportation planning.

BIM and sensor-based data management system for construction safety monitoring

Purpose This research aims to investigate the integration of real-time monitoring of thermal conditions within confined work environments through wireless sensor network (WSN) technology when integrated with building information modelling (BIM). A prototype system entitled confined space monitoring system (CoSMoS), which provides an opportunity to incorporate sensor data for improved visualization through new add-ins to BIM software, was then developed. Design/methodology/approach An empirical study was undertaken to compare and contrast between the performances (over a time series) of various database models to find a back-end database storage configuration that best suits the needs of CoSMoS. Findings Fusing BIM data with information streams derived from wireless sensors challenges traditional approaches to data management. These challenges encountered in the prototype system are reported upon and include issues such as hardware/software selection and optimization. Consequently, various database models are explored and tested to find a database storage that best suits the specific needs of this BIM-wireless sensor technology integration. Originality value This work represents the first tranche of research that seeks to deliver a fully integrated and advanced digital built environment solution for automating the management of health and safety issues on construction sites.