Embedded Sensors Research Articles

Quality-Driven Online Task-Bundling-Based Incentive Mechanism for Mobile Crowdsensing

With the advancement of wireless communication technologies and rich embedded sensors in smart mobile devices, mobile crowdsensing has become an attractive paradigm for collecting sensing data from surrounding environments. Effective incentive mechanisms often play an important role in guaranteeing high quality of services and wide involvement of workers. Much work has been carried out in this aspect to encourage workers to strenuously and truthfully provide high quality data. However, task quality does not only depend on workers’ subjective effort or truthfulness, but also workers’ distribution and task quality growth law. In this paper, we consider a scenario where worker arrivals are unevenly distributed and the growth of task quality conforms to the law of diminishing margin of workers’ efforts. We propose a Quality-driven Online Task-Bundling-based incentive mechanism (QOTB). The design objective is to maximize the social welfare while maximally satisfying the task quality requirements. In QOTB, we introduce Mental accounting Theory to build accounts for task execution profit and bonus, respectively, which are then used to derive the participation willingness of workers. We adopt task bundling to stimulate workers to change their original travel schedules for balancing the task participations according to the popularities of task locations and also the traveling cost. We present the detailed mechanism design of QOTB. We prove that the QOTB mechanism has desired properties of willingness truthfulness, individual rationality, and computation efficiency. We conduct extensive simulations and the results show that QOTB can effectively improve the social welfare while satisfying the task quality requirements.

Quantitative Analysis of Internet of Things Technology on the National Economic Accounting: A Prediction Model Based on the FCM-BP Neural Network Algorithm.

The development of Internet of Things (IoT) technology is of great significance for modern financial settlements based on information technology. The IoT technology, which can provide a convenient approach for accounting, has the advantages of intelligent processing, reliable transmission, and comprehensive perception. For national economic accounting, a new integrated system for monitoring the environment is developed and designed by using embedded development technology and sensor technology. The system uses a wireless sensor network environment monitoring system IoT platform with embedded internal processors. Analyze and design the system as a whole, including the construction of the basic platform of the system, the design of the internal plates and circuits of the system, the monitoring design of the input node, and the monthly design of the output interface calculation. Finally, a physical model is built, and data measurement and analysis are carried out under different conditions, and the evaluation and advantage analysis of the system's operating status are given. The system can carry out all-round, multilevel and three-dimensional real-time monitoring of the construction site environment, including dust, PM2.5, temperature, humidity, wind speed, carbon dioxide, and other indicators in the construction site environment. In addition, the system can upload various monitoring data to the detection system through the internal network. The system has the functions of monitoring, alarming, recording, querying, and counting of the target monitoring station and can also be linked with the environmental control device. The construction site staff can conduct real-time supervision through the mobile terminal and computer terminal management platform. In addition, it can also meet the role of real-time remote monitoring and online guidance and regulation. It has reference value for the safety and management of the actual operation process of the project.

Underground Coal Mine Environmental Multi-Parameter Monitoring Module with Embedded Sensors: Application of UHF Radio Transceiver Device

Underground Coal Mine Environmental Multi-Parameter Monitoring Module with Embedded Sensors: Application of UHF Radio Transceiver Device

Design of Building Environment Detection System for Architectures Based on Internet of Things.

In the process of urban building design, a new integrated system for monitoring the environment is developed and designed by using embedded development technology and sensor technology. The system uses a wireless sensor network environment monitoring system IoT platform with embedded internal processors. Analyze and design the system as a whole, including the construction of the basic platform of the system, the design of the internal plates and circuits of the system, the monitoring design of the input node, and the monthly design of the output interface calculation. Finally, a physical model is built, and data measurement and analysis are carried out under different conditions, and the evaluation and advantage analysis of the system's operating status are given. The system can carry out all-round, multilevel, and three-dimensional real-time monitoring of the construction site environment, including dust, PM2.5, temperature, humidity, wind speed, carbon dioxide, and other indicators in the construction site environment. In addition, the system can upload various monitoring data to the detection system through the internal network. The system has the functions of monitoring, alarming, recording, querying, and counting of the target monitoring station and can also be linked with the environmental control device. The construction site staff can conduct real-time supervision through the mobile terminal and computer terminal management platform. In addition, it can also meet the role of real-time remote monitoring and online guidance and regulation. It has reference value for the safety and management of the actual operation process of the project.

BIKES: Bicycle Itinerancy Kalman Filter With Embedded Sensors for Challenging Urban Environment

The use of bicycles is regaining popularity, especially in city centers where they can be used as quickly as cars and reduce carbon footprint. However, in these dense urban environments, navigation methods based on GNSS technologies do not provide sufficient accuracy for cyclist navigation and safety. To mitigate the challenges of indoor-like surroundings, a new positioning algorithm: BIKES (Bicycle Itinerancy Kalman filter with Embedded Sensors) was developed. This extended Kalman filter processes deeply degraded GNSS data to update velocity and position estimates with differential computation approaches working even in degraded environments. GNSS signals are combined with inertial and magnetic data to continuously estimate the trajectory when GNSS is unavailable. BIKES’ performance was tested in real-life conditions on a 3 km long path in the city center downtown Nantes and compared to Google Fused Location Provider estimates. A mean positioning error below 1 m with a 0.5 m standard deviation is achieved. These results are 4 times better than the Google solution. This algorithm allows also distinguishing if the cyclist is riding on a bike path, the sidewalk, or the road, which is critical for guidance systems.

Establishment of physiologically relevant oxygen gradients in microfluidic organ chips.

In vitro models of human organs must accurately reconstitute oxygen concentrations and gradients that are observed in vivo to mimic gene expression, metabolism, and host-microbiome interactions. Here we describe a simple strategy to achieve physiologically relevant oxygen tension in a two-channel human small intestine-on-a-chip (Intestine Chip) lined with primary human duodenal epithelium and intestinal microvascular endothelium in parallel channels separated by a porous membrane while both channels are perfused with oxygenated medium. This strategy was developed using computer simulations that predicted lowering the oxygen permeability of poly-dimethylsiloxane (PDMS) chips in specified locations using a gas impermeable film will allow the cells to naturally decrease the oxygen concentration through aerobic respiration and reach steady-state oxygen levels <36 mm Hg (<5%) within the epithelial lumen. The approach was experimentally confirmed using chips with embedded oxygen sensors that maintained this stable oxygen gradient. Furthermore, Intestine Chips cultured with this approach supported formation of a villus epithelium interfaced with a continuous endothelium and maintained intestinal barrier integrity for 72 h. This strategy recapitulates in vivo functionality in an efficient, inexpensive, and scalable format that improves the robustness and translatability of Organ Chip technology for studies on microbiome as well as oxygen sensitivity.

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

MOBILE CROWDSENSING FRAMEWORK FOR ROAD SURFACE QUALITY DETECTION.

Smartphones became ubiquitous and are used by so many people, at least to know the driving directions to their destination. Smartphones come with rich embedded sensors (e.g., GPS, accelerometer, and camera) as well as built-in radios (e.g., Bluetooth, Wi-Fi, and Cellular), which both enable users to gather data and distribute it among people at any time or location. These features have come up with the mobile crowdsensing (MCS) development which can be used in a wide range of applications. In this paper, we introduce a complete mobile crowdsensing framework for road surface condition detection. Various modules have been addressed such as task management, data fusion, reputation scoring, incentive awarding, security and privacy, as well as discussing popular techniques and algorithms utilized in the proposed MCS framework modules. A prototype of the crowd sensing application is designed which is related to our framework. The proposed framework considers the data quality and trustiness between the users and the server as well.

Performance of cement composite embeddable sensors for strain-based health monitoring of in-service structures

There is a growing need to develop sensors which can be embedded into the structures during the construction stage itself for developing smart structures. It is preferred to develop these kinds of sensors with the material same as that of material used in construction for the sake of compatibility and better capturing the actual state of distress in the structure. Towards this, in this study cement based piezo-resistive sensors are developed with the help of conductive nano-fillers (Carbon Nanotubes (CNTs)). Since the sensors are cement based, and porous in nature, the characteristics of the sensor will vary due to water penetration into the sensor. As the structures with such embedded sensors have to perform for years, understanding the variations in the characteristics of the sensor due to pore structure is very important. In this regard, the conductivity of the sensor is assessed where the effect of dosage of CNTs, functionalization of CNTs, type of electrical conductivity measurement (both DC and AC) and pore water are the parameters. The strain sensitivity of the sensors under cyclic stress is also investigated and reported in the present study. The findings of this study will help in developing continuous health monitoring strategies using highly sensitive embeddable cement-based nanocomposites.

First Clinical Experience With the Pressure Sensor-Based Autoregulation of Blood Flow in an Artificial Heart.

The CARMAT-Total Artificial Heart (C-TAH) is designed to provide heart replacement therapy for patients with end-stage biventricular failure. This report details the reliability and efficacy of the autoregulation device control mechanism (auto-mode), designed to mimic normal physiologic responses to changing patient needs. Hemodynamic data from a continuous cohort of 10 patients implanted with the device, recorded over 1,842 support days in auto-mode, were analyzed with respect to daily changing physiologic needs. The C-TAH uses embedded pressure sensors to regulate the pump output. Right and left ventricular outputs are automatically balanced. The operator sets target values and the inbuilt algorithm adjusts the stroke volume and beat rate, and hence cardiac output, automatically. Auto-mode is set perioperatively after initial postcardiopulmonary bypass hemodynamic stabilization. All patients showed a range of average inflow pressures of between 5 and 20 mm Hg during their daily activities, resulting in cardiac output responses of between 4.3 and 7.3 L/min. Operator adjustments were cumulatively only required on 20 occasions. This report demonstrates that the C-TAH auto-mode effectively produces appropriate physiologic responses reflective of changing patients’ daily needs and represents one of the unique characteristics of this device in providing almost physiologic heart replacement therapy.

Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study.

Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance. The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process. QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others. The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall. The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation.

Continual learning in sensor-based human activity recognition: An empirical benchmark analysis

Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an important question arises: how can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed. This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain. To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets. Then, we present a comprehensive empirical analysis of their computational cost and of their effectiveness of tackling HAR specific challenges (i.e., sensor noise and labels’ scarcity). The presented results uncover useful insights on their applicability and suggest future research directions for HAR systems.

Tracking major resources for automated progress monitoring of construction activities: masonry work case

PurposeThe purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction.Design/methodology/approachAn automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity.FindingsThe prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used.Originality/valuePrevious studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.

RETRACTED: Research on monitoring the load of short-distance orienteering sports on campus based on embedded system acceleration sensor

Orienteering means that competitors need to be identified, with a specific focus on the city on a predetermined terrain or with the help of a guide, in this game, which is predicted by the execution of time, their ability to integrate their actual perseverance with the mental cycles and adjust to the environment and regulate them effectively. Acceleration Sensors observe these decisions in the selected courses, and each course has its environmental characteristics. Subsequently, competitors must disassemble these features well and choose the course that campus-based embedded system. In the short distance, orienteering sports components that affect course selection will be examined. For this reason, competitor's information is obtained by having watched an orienteering race organized by the Federation. The information collected verified through quantitative and subjective strategies, and an overall understanding of competitors 'behavior obtained and qualification of moldable and subject-subordinate components in competitors' dynamics. From the model able parts, a model created that recorded the separation and the brief separation based on the landscape surface and, subsequently, analyzed its similarity with the competitors' practices. Moreover, the results were justified by different factual observations. According to Data Pathway Algorithm, the effects of the investigation, environmental factors play a significant role in competitors' dynamics. The model implementation is much more accurate in short-segmented courses than insignificant distance courses that reduce the psychological burden.

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

Soft robotic sensors, a class of pliable, embeddable sensors, are well-suited for applications in wearable technology because of their ease of integration with common clothing articles. The suitability of soft robotic sensors for estimation of human joint angles has been proven; this research represents another step towards development of a reliable laboratory data collection platform for the human ankle joint complex. In this research, the accuracy and repeatability of a newly-developed wearable prototype are evaluated as a potential replacement for camera-based motion capture by measuring differences between simultaneously collected motion capture and stretch sensor data. The accuracy of these measurements is compared to measurements collected using a previous prototype for validation. Results show that the newly-developed prototype is capable of joint angle estimation within 1.86° mean-absolute-error during complex, dynamic movements and that wearing shoes over the sock prototype does not significantly degrade performance.

A Survey on the Integration of Blockchain With IoT to Enhance Performance and Eliminate Challenges

Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people’s daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain’s proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.

Secure Three‐Factor Anonymous User Authentication Scheme for Cloud Computing Environment

Cloud computing provides virtualized information technology (IT) resources to ensure the workflow desired by user at any time and location; it allows users to borrow computing resources such as software, storage, and servers, as per their needs without the requirements of complicated network and server configurations. With the generalization of small embedded sensor devices and the commercialization of the Internet of Things (IoT), short‐ and long‐range wireless network technologies are being developed rapidly, and the demand for deployment of cloud computing for IoT is increasing significantly. Cloud computing, together with IoT technology, can be used to collect and analyse large amounts of data generated from sensor devices, and easily manage heterogeneous IoT devices such as software updates, network flow control, and user management. In cloud computing, attacks on users and servers can be a serious threat to user privacy. Thus, various user authentication schemes have been proposed to prevent different types of attacks. In this paper, we discuss the security and functional weakness of the related user authentication schemes used in cloud computing and propose a new elliptic curve cryptography‐ (ECC‐) based three‐factor authentication scheme to overcome the security shortcomings of existing authentication schemes. To confirm the security of the proposed scheme, we conducted both formal and informal analyses. Finally, we compared the performance of the proposed scheme with those of related schemes to verify that the proposed scheme can be deployed in the real world.

A Proximity Sensor for the Steering Wheel Based on Leaky Coaxial Cable

A practical readily embeddable proximity sensor based on a leaky coaxial cable (LCX) is proposed for detecting the presence and position of hands on a steering wheel. Different LCX configurations are analyzed both numerically and experimentally. For numeric simulations, the finite-difference time-domain (FDTD) method is applied to a square cross-section leaky coaxial cable. A comparable-sized 3D-printed LCX with various configurations is also measured to verify the design of the sensor. Based on those numerical and experimental results, a prototype steering wheel proximity sensor is designed, fabricated, and then embedded into a Honda 2013 Acura MDX steering wheel. Through the use of a network analyzer and the inverse discrete Fourier Transform, the hand position information is obtained.

Objective measurement of adherence to wearing foot orthoses using an embedded temperature sensor

The objective of this study was to evaluate the validity of a temperature sensor for the measurement of adherence to wearing foot orthoses. Ten participants were provided with foot orthoses containing an embedded temperature sensor and wore the orthoses for a randomly-determined duration over a five-day period. Sensor-detected wear time was compared to a reference standard (objectively measured wear time using a smart-phone application). Ambient temperature and physical activity were recorded with a temperature gauge and wearable activity tracker, respectively. A simple peak detection algorithm which identified the largest one-minute changes in sensor temperature provided highly accurate wear time values (r = 0.999, coefficient of variation=0.2%). Ambient temperature and physical activity did not significantly influence temperature sensor scores. These findings demonstrate that the temperature sensor provides accurate foot orthosis wear time data and may therefore be a useful tool for documenting adherence in clinical practice and intervention studies.

Adaptive Compressive Video Coding for Embedded Camera Sensors: Compressed Domain Motion and Measurements Estimation

This paper presents a new framework for wireless video streaming services using the resource-constrained embedded camera sensors based on block compressive sensing. We propose adaptive encoding scheme that exploits high redundancy between successive video frames to reduce transmission cost while effectively handling occlusion effects and, at the same time, maintains the simple and energy conserving encoder design. The proposed methodology adapts the compression ratio for different blocks of the non-key frame depending on temporal correlation. We also propose compressed domain motion and measurements estimation techniques to exploit the high correlation between successive frames at the decoder. The proposed motion estimation technique makes use of restricted isometry property of the sensing matrix to seek the best matching measurement vector for motion estimation as opposed to block matching in conventional video coding. In the proposed measurement estimation technique, efficient utilization of bandwidth is achieved by skipping some measurements at the transmitter side. The skipped measurements are estimated at the receiver by exploiting the correlation between CS measurements of the non-key frame and corresponding motion predicted frame using multiple regression model. Extensive simulation results on a set of diverse video sequences are presented to demonstrate the effectiveness of the proposed video coding technique.