Mobile Sensor Research Articles

Sandwiched film of graphene/silver nanowire conductive layer reinforced by hydroxyethyl cellulose bond layer

Multilayer nanocomposite film made of different materials has multifunctional properties and is applied in the field of flexible electronic devices. Herein, hydroxyethyl cellulose (HEC) and boron nitride nanosheets (BNNS) were used as the matrix and thermal conductivity material of the HEC/BNNS (HB) insulation layer and were combined with conductive blade structure graphene/silver nanowires (GA) film to prepare a three-layer HB/GA20/HB film. Using the high mechanical properties of the HEC based film, the tensile strength of the three-layer film is increased to 22.0 MPa, 633 % higher than that of the pure conductive film. The sensor prepared by multilayer film has good bending sensing performance (1500 cycles) and electromagnetic shielding performance (29.3 dB). The heating temperature of HB/GA20/HB film heater is up to 107.9 °C at 20 V. In the HB/GA20/HB film, the external HB layer provides insulation, thermal conductivity and physical support, and the internal GA layer with good conductive and sensing properties is combined to build a multi-functional sensor, which can be applied as a mobile sensor, heater and electromagnetic shielding material in the flexible wearable field.

Tomato maturity recognition with convolutional transformers

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold: firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively. This work can potentially improve the efficiency and accuracy of tomato harvesting, grading, and quality control processes.

OUT-OF-HOME MOBILITY, DAILY WORKING MEMORY, AND NEIGHBORHOOD WALKABILITY IN HEALTHY OLDER ADULTS

Abstract Out-of-home mobility could enhance cognitive performance, but environmental contexts — such as neighborhood walkability — may influence the associations. This study examined daily out-of-home mobility in relation to daily cognitive performance in healthy older adults and the moderator effects of neighborhood walkability therein. Participants wore a custom-built mobile GPS sensor („uTrail“) and completed smartphone-based working memory assessments for 15 days, and responded to the baseline Neighborhood Environment Walkability Scale. Analyses included 947 days’ data from 109 Swiss older adults aged 65 to 89 years. Out-of-home mobility (i.e., out-of-home time, maximum distance from home) were extracted from the GPS data. Multilevel models showed that daily out-of-home time and daily maximum distance from home were not associated with daily working memory performance across the study sample, but the associations were moderated by neighborhood walkability. Out-of-home time and maximum distance from home were positively (vs. negatively) associated with working memory performance in participants who lived in neighborhood with higher (vs. lower) street connectivity and more (vs. less) access to places for walking and cycling. Additionally, maximum distance from home were positively (vs. negatively) associated with working memory performance in participants in neighborhood with lower (vs. higher) mixture of land use. To conclude, spending more time out of home and travelling longer distance away from home could be beneficial for daily cognitive performance of older adults who lived in well-connected and higher-walkable and -bikeable neighborhood. Travelling longer distance could also benefit those who lived in a neighborhood with a lower mixture of land use.

Design Guidelines for Improving Mobile Sensing Data Collection: A Prospective Mixed-Methods Study (Preprint)

Design Guidelines for Improving Mobile Sensing Data Collection: A Prospective Mixed-Methods Study (Preprint)

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Wireless sensor networks, have drawn a lot of interest because of their adaptability and range of uses in different industries. WSNs face significant challenges when it comes to energy efficiency because sensor nodes are usually battery-powered and have limited resources. Several energy-efficient methods and protocols, such as duty cycling, data aggregation, and topology management, have been put forth to address this problem. Moreover, new potential for mobile wireless sensor networks are presented by the integration of WSNs with mobile and static devices, such as drones, tablets, and smartphones. In this study, we suggest utilizing Zigbee technology to establish a robust and flexible monitoring system for both stationary and mobile sensors. Numerous industries, including healthcare, smart agriculture, asset tracking, energy management, smart home automation, and industrial monitoring and control, have made extensive use of Zigbee. By leveraging Zigbee's capabilities, we hope to improve the protocol's performance while establishing dependable communication links between nodes, analyzing the communication range, and assessing the influence of environmental conditions. In this study, a system model for Zigbee deployment in mobile robots will be presented. It will address the basics of Zigbee, communication difficulties, networking with Zigbee, and simulations or real-world outcomes. We will learn about the strengths and weaknesses of Zigbee-based systems in terms of creating reliable communication links in mobile wireless sensor networks by looking at their architecture and functionality. The results of this study will help us comprehend Zigbee's potential to improve monitoring systems and make better decisions across a range of industries. The study's emphasis on mobile monitoring systems signifies a step forward in addressing the evolving needs of wireless sensor networks in dynamic environments.

Detection of Suspicious Activity using Mobile Sensor Data and Modified Sub-space K-NN for Criminal Investigations

With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. This study lays the foundation for future explorations, envisioning potential applications in diverse fields, including zoology. By adapting and expanding the proposed methodology, researchers in zoology could harness mobile sensor data to study animal behavior, offering an innovative approach to understanding and monitoring wildlife activities. Such interdisciplinary bridges highlight the versatility of technological advancements, where tools developed for criminal investigations may find unexpected yet valuable applications in the study of zoological phenomena

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

Recurrent Event Analysis in the Presence of Real-Time High Frequency Data via Random Subsampling

Digital monitoring studies collect real-time high frequency data via mobile sensors in the subjects’ natural environment. This data can be used to model the impact of changes in physiology on recurrent event outcomes such as smoking, drug use, alcohol use, or self-identified moments of suicide ideation. Likelihood calculations for the recurrent event analysis, however, become computationally prohibitive in this setting. Motivated by this, a random subsampling framework is proposed for computationally efficient, approximate likelihood-based estimation. A subsampling-unbiased estimator for the derivative of the cumulative hazard enters into an approximation of log-likelihood. The estimator has two sources of variation: the first due to the recurrent event model and the second due to subsampling. The latter can be reduced by increasing the sampling rate; however, this leads to increased computational costs. The approximate score equations are equivalent to logistic regression score equations, allowing for standard, “off-the-shelf” software to be used in fitting these models. Simulations demonstrate the method and efficiency-computation tradeoff. We end by illustrating our approach using data from a digital monitoring study of suicidal ideation. Supplementary materials for this article are available online.

Detecting and recovering critical mineral resource systems using broadband total-field airborne natural source audio frequency magnetotellurics measurements

Airborne geophysical methods offer a substantial advantage compared to ground-based techniques in exploring territories of different sizes, ranging from entire metallogenic provinces to the deposit scale, including those hosting critical minerals. An airborne method with measurements of natural magnetic field variations, known as audio frequency magnetotellurics (a passive field method), significantly increases the depth of investigation and expands the resistivity detection range compared with some controlled-source primary-field methods. We describe the technical solutions used in an airborne electromagnetic passive system with a mobile sensor of the total magnetic field variations and the stationary sensor of electric field variations, and its applications to recovering the complex geology of hydrothermal-magmatic systems often associated with critical minerals. The system’s ability to explore depths, typically beginning from the near-surface and down to 1–2 km, by recording responses in three orthogonal inductive coils over a broad bandwidth from 22 Hz to 21,000 Hz allows for mapping resistivities across a broad range. This capability is crucial for obtaining more comprehensive exploration models. Field case studies of the natural field system include application in exploring for unconformity uranium mineralization, along with other associated minerals, epithermal gold and polymetallic-bearing structures, and ferromanganese and polymetallic deposits formed in a continental rift valley. An extra case study involving kimberlites was incorporated as a proven example of the natural field system’s capability in conducting near-surface and deep investigations. The case histories illustrate the airborne natural electromagnetic field technology capabilities in recovering geoelectric models and their specific patterns.

CAGSI: A Classification Approach towards Gait Speed Identification

The last few decades have witnessed a remarkable amount of research addressing numerous challenges in the domain of human activity recognition. One popular problem in this domain has been that of gait analysis. A subproblem in this domain is to identify the speed of a mobile object through gait analysis. Apart from clinical diagnostic applications, the detection of the speed of a person is also important in remote health monitoring, tracking of the mentally incompetent, and determining proper ambulatory assistive devices for the orthopaedically impaired. Gait analysis-related problems commonly deal with large volumes of interrelated data for which machine-learning techniques have been proven effective. However, the size of the feature set used in such problems is a crucial factor. The choice of a large feature set may complicate the approach for long-term analysis. The present work addresses the problem of human walking speed classification through the machine learning approach. Data was experimentally collected with the mobile phone sensors carried by volunteers of different physiques. Only the acceleration readings along the three axes of the accelerometer are considered for further experimentation. Although walking speed is a personal trait, four classes of data have been curated, namely, slow walking, moderate walking, fast walking, and sitting. The speeds of the walks were not pre-defined so the volunteers performed the walks as per their own comfort, which enhances the challenge of distinguishing between sensor signals of varying speed. Experiments have been performed using different supervised learning algorithms with only acceleration data. The performance of the learning models has been analyzed with the help of accuracy, precision, recall, f1-score, and the ROC curve in a One-vs-Rest approach. The results demonstrate that the performance of this system for walking speed identification is comparable to state-of-the-art works. Our work has a unique perspective as it uses a primary dataset comprising only three features.

A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications

Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving energy of up to 92% at 4,500 rounds.

Design of routing protocol for enhancing quality of service in wireless ad hoc and sensor netw ork: LEQA

<p>Wireless sensor networks (WSNs) are now adopting mobile sensors due to their increased popularity in research and industry. To enhance WSNs' performance, mobility can be utilized to gather data. But, if the collector's route is fixed and movement is not manageable, current quality of service (QoS) strategies and protocols are ineffective in achieving timely data delivery while maintaining energy efficiency. In the real world, WSN networks use both actuator - actuator and sensor - actuator coordination. To conserve energy in communicati on tasks with heavy traffic and high volume, sensors/actuators can be relocated to desired locations. This study introduces a routing protocol that optimizes delivery latency and energy conservation in WSNs. The proposed latency, energy, and quality of ser vice aware (LEQA) protocol uses a cooperative approach to track the sink and coordinate communication between sensors and actuators. Each sensor schedules its time division multiple access (TDMA) to improve QoS metrics such as low energy consumption, low l atency, or packet loss. It also addresses sensor - actuator coordination and proposes a data communication protocol for efficient and fast communication with actuator nodes. This reduces energy consumption and minimizes latency.</p>

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Recent Investigations in Wireless Sensor Networks on Distributed Defense Mechanism for Clone Attacks

Regarding accelerating development of mobile sensor nodes technology, increasing the utilization of them, and also facing with security challenges in these networks; specially clone nodes attack, this paper focuses on exploiting optimum criteria of node clone intrusion detection procedures in mobile wireless sensor networks by using experimental analysis of procedures. Since many of recommended protocols in this area have not been experimentalised, also no comprehensive study has been performed on the possibility and capability of these procedures; in this paper types of sensor network architecture, with the presence of mobile sensor node, are analyzed. Then according to the type of architecture, the procedures of clone node intrusion detection is classified and meticulously scrutinized. Besides, due to measuring the efficiency, exploiting the optimum parameters and also appraising the expenses of procedures, Finally, the conclusion based on theoretical analysis and simulation is presented

Time and Space Domain Prediction of Water Quality Parameters of Bagmati River Using Deep Learning Methods

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization.

Estimation of annual sound pressure levels based on mobile measurements

In order to compute the population exposure to environmental noise and to design and apply convenient action plans to reduce it, European Directive 2002/49/EC defines an environmental noise map as a picture of noise levels for different sources and time intervals. According to the Directive, since 2019 it is mandatory to compute noise maps by simulation, using the model CNOSSOS-EU. However, with the fast development of large sensor networks, in a near future, it can become cheaper and more convenient to compute noise maps by taking direct measurements. European Directive 2002/49/EC requires that noise maps plot the annual values of sound pressure level. Previous studies have shown that it is possible to estimate the annual sound pressure levels with good accuracy using a sample of only 7-10 days. The present contribution explores the possibility of estimating the annual sound pressure levels with random samples of a few seconds or minutes taken on separate random days. This kind of temporal sampling is possible by using mobile sensors moving throughout a city.

Retracted: Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Retracted: Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space