Mobile Sensor Research Articles

Providing an effective key management scheme to increase transaction security of homogeneous mobile wireless sensor networks

Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14% . 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review.

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

Mobile Structural Health Monitoring Based on Legged Robots

With the advancements in information, communication, and sensing technologies, structural health monitoring (SHM) has matured into a substantial pillar of infrastructure maintenance. In particular, wireless sensor networks have gradually been incorporated into SHM, leveraging new opportunities towards reduced installation efforts and enhanced flexibility and scalability, as compared to cable-based SHM systems. However, wireless sensor nodes are installed at fixed locations and need to be employed at high density to reliably monitor large infrastructure, which may cause high installation costs. Furthermore, the limited power autonomy of wireless sensor networks, installed at fixed locations for unattended long-term operation, still represents a significant constraint when deploying stationary wireless sensor nodes for SHM. To resolve the critical constraints stemming from costly high-density deployment and limited power autonomy, a mobile structural health monitoring concept based on legged robots is proposed in the study reported in this paper. The study explores the accuracy and cost-efficiency of deploying legged robots in dense measurement setups for wireless SHM of civil infrastructure, aiming to gain insights into the advantages of mobile wireless sensor nodes in general and of legged robots in particular, in terms of obtaining rich information on the structural condition. As is shown in this paper, the legged robots, as compared to stationary wireless sensor nodes, require a smaller number of nodes to be deployed in civil infrastructure to achieve rich sensor information, entailing more cost-efficient, yet accurate, SHM. In conclusion, this study represents a first step towards autonomous robotic fleets advancing structural health monitoring.

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Distributed Multi-Agent Hierarchy Construction for Dynamic DCOPs in Mobile Sensor Teams

Coordinating multiple agents to optimize an objective has several real-world applications. In areas such as disaster rescue, environment monitoring and the like, mobile agents may be deployed to work as a team to achieve a joint goal. Recently, multi-agent problems involving mobile sensor teams have been formalized in the literature as DCOP_MSTs. Under this class of problems, DCOP algorithms are applied to enable agents to coordinate the assignment of their physical locations as they jointly optimize the team objective. In DCOP_MSTs, the environment is dynamic, and agents may leave or join the environment at random times. As a result, a predefined interaction topology or graph may not be useful over the problem horizon. Therefore, there is a need to study methods that could facilitate agent-to-agent interaction in such open and dynamic environments. Existing methods require reconstructing the entire graph upon detecting changes in the environment or assume a predefined interaction graph. In this study, we propose a dynamic multi-agent hierarchy construction algorithm that can be used by DCOP_MST algorithms that require a pseudo-tree for execution. We evaluate our proposed method in a simulated target detection case study to show the effectiveness of the proposed approach in large agent teams.

Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks.

Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage.

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City.

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

A DCNN-LSTM based human activity recognition by mobile and wearable sensor networks

Systems for recognizing human activities are being developed as part of a larger framework that will allow for continuous monitoring of human behavior in intelligent home environments for elderly care, ambient assisted living, rehabilitation, sports injury detection, surveillance, and entertainment. The most challenging component of the sensor-based human activity detection pipeline for mobile and wearable devices is extracting relevant features. Using mobile and wearable sensors, the most challenging step in the pipeline for recognizing the behaviors of humans is extracting relevant features. The algorithm's performance is affected by feature extraction, which also reduces computation complexity and time. However, existing methods of recognizing human activity rely on hand-crafted characteristics that cannot handle complicated behaviors. There are several known systems for recognizing individual activities, including deep learning, but more must be developed to recognize transitions between actions. To solve these challenges, a new deep learning-based system is proposed for recognizing human actions in this research. Initially, a fuzzy logic-based genetic algorithm (FGA) is used for extracting features from sensor data. The introduced feature selection approach, called PSOACO, aims to enhance the particle swarm optimizer (PSO) algorithm's performance through the ant colony optimizer (ACO) operators. Then, the deep learning approach, named Deep Convolutional Neural Network and Long Short Term Memory (DCNN-LSTM), is used to classify the activities. F1 Score, Recall, Precision, and Accuracy are used to analyze the implementation's effectiveness. The overall recognition accuracy of the proposed model for UCI-HAR, PAMAP2, and WISDM datasets is 99.92%, 99.86%, and 99.94 %. According to the implementation, the HAR with privacy performs better than existing methods done on the Python platform.

The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects.

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause-effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners' practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis.

Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network

Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network

Trends in Development of Applied Social and Communication Technol-Ogies in Medical Field: Mobile Applications and Programmes in Healthcare

The purpose of the article is to conduct a comprehensive analysis of the trends in the development of applied social and communication technologies in the medical sector, in particular, to trace dynamics of growth in the number of mobile applications and programmes in healthcare sector. The research methodology is formed by general scientific and special methods of studying the problem, the use of which allowed the authors to achieve the goal. The combination of the methods used helped to clarify the logical sequence and trends in the development of applied social and communication technologies in the modern information space of society. The basic principle was the principle of historicism, which implies consideration of any phenomenon in its self-development under the influence of various factors. The historical and systemic method is aimed to analyse society as a whole and the transformations within it caused by changes in the information and communication space. Quantitative methods are used to process the results of analytical research and to diagnose the phenomena under study. The scientific novelty of the work is to expand the understanding of the trends in the development of applied social and communication technologies in the medical field. The authors' study analysed modern domestic medical information systems, the possibilities of their interaction with eHealth, and identified priority technological areas in the medical sector, such as virtual reality, 3D printing, robotics, artificial intelligence, augmented reality, a global network of medical facilities, wearable devices, smart technologies, and precision (personalised) medicine. The authors state that in recent years, attention has increased to applications, mobile sensors and digital media in order to get help in maintaining one's health. Conclusions. The study has proved the positive dynamics in the growth of mobile applications and programmes in healthcare sector and determined the trends of the further development of information technologies in the medical sector. Key words: healthcare system, information technologies, medical information systems, mobile applications in healthcare.

Clustering Algorithms and Optimization Strategies in WSNs: A State-of-the-Art Review

Wireless Sensor Networks (WSNs) have emerged as a groundbreaking technology for collecting data on various physical phenomena, offering the potential to revolutionize infrastructure systems. The paper provides an exhaustive exploration of the existing literature on clustering algorithms within WSNs and the diverse optimization techniques proposed for cluster head selection. A comprehensive analysis of various routing protocols, mobility-based solutions, and energy conservation strategies is presented, shedding light on their respective strengths and limitations. The examination encompasses a wide spectrum of approaches, including hierarchical cluster-based routing, mobile sensor integration, and energy-efficient techniques inspired by industry standards like ZigBee. The paper further explores ecological monitoring applications, redundancy reduction mechanisms, and power- efficient hierarchical routing protocols, offering valuable insights into their contributions to the field. This review paper aims to consolidate and synthesize the wealth of knowledge in the domain of clustering algorithms and optimization techniques for WSNs. By assessing the strengths and weaknesses of these methodologies, it seeks to provide a comprehensive resource for researchers and practitioners in the field, ultimately contributing to the advancement of energy-efficient and reliable wireless sensor networks. Keywords— APTEEN, EARP, ECCP, GPS, HEED, LEACH, PEGASIS, TEEN, WSN.

Adaptive Techniques for the Online Estimation of Spatial Fields With Mobile Sensors

The regression-based reconstruction of spatial fields by static sensors requires the number of measurements be at least equal to the number of unknown parameters in order to provide a unique solution. Improvements include the use of mobile sensor networks to arrive at higher accuracy estimates. An alternate approach utilizes adaptive techniques to update the parameter estimates online. Such adaptive techniques need persistence of excitation to ensure convergence of the parameter and function estimates. It is shown that the motion of just a single sensor is a necessary condition for persistence of excitation and, subsequently, of function convergence. Having a prescribed sensor guidance is also a sufficient condition for function convergence. First, a Lyapunov-based adaptation and guidance is presented and it is shown that persistence of excitation necessitates the time dependence of the outer product of the regressor vector with itself and which in turn requires the motion of the sensor within the spatial domain. Subsequently, a gradient-based adaptive scheme relaxes the Lyapunov-based sensor guidance to any user-defined guidance as long as the outer product of the associated rank-one regressor vector with itself has a uniformly positive definite integral. The latter adaptation is conducive to the inclusion of platform dynamics resulting in a vehicle control design needed to attain parameter convergence. Extension of the centralized solution to distributed estimation using a network of mobile sensors is also examined. These results are demonstrated with simulations in one and two dimensions.

Using and Testing Camera Sensors with Different Devices at Cosmic Ray Detection

Cosmic Ray (CR) is high-energy charged particles that reach the earth from space. CR detection methods and studies have been progressing rapidly since the beginning of the 20th century. One of these methods is the use of digital cameras with Charge Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) sensors. Mobile phone cameras or webcams offer an easily using and economical measurement system for CR measurement. The sensors are exposed to CR during a long exposure. CRs leave traces in the background. Cosmic particle tracks are then separated from the background noise and can be classified. Making the traces of the particles visible is important for understanding the subject. In this context, traces of particles such as electrons, muons, and alphas can be seen with the cloud chamber experiments. Help of sensor technology and cameras have developed in recent years, CR traces can be easily detected so that it can be seen. There are many software and international projects that detect CR using CMOS sensors in cell phone cameras. In this study, related projects, programs and studies were researched; CR traces that we captured with the help of Cosmic-Ray Extremely Distributed Observatory (CREDO) and Cosmic Ray Finder (CRF) software with web cam and a mobile phone cam CMOS sensor are presented. Links have been made about astrophysical events coinciding with previously detected particle images.

Regular Vehicle Spatial Distribution Estimation Based on Machine Learning

For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to investigate the hidden relationship between the interaction information of adjacent CVs and the spatial distribution of RVs among CVs, the Gaussian mixture model-hidden Markov model (GMM-HMM) is selected as the identification method. Then, three sets of experiments were designed to study the influence of observed features on the identification capability of the model, generalization capability validation, and comparison with other methods, respectively. Finally, the proposed method is verified by the dataset generated by the car-following model. The experimental results show that selecting the relative position and time headway as observed features can effectively reflect the regular vehicle spatial distribution between adjacent CVs. The average accuracy of the proposed method to identify the regular vehicle spatial distribution is over 93.7%, which can provide valuable suggestions for the Internet of Vehicles application.

An Enhanced Food Digestion Algorithm for Mobile Sensor Localization.

Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.

Non-Destructive Determination of Plant Pigments Based on Mobile Phone Data

This paper proposes methods and tools for determining plant pigments using data from a mobile phone video sensor. A disadvantage of the known studies in this field is that they are mainly aimed at determining the chlorophyll content. There are few studies related to the determination of pigments such as carotenoids, flavonoids, and betalains, which are also important in terms of determining the condition of plants in their cultivation. Cucumbers were chosen because the long periods of drought in Bulgaria, lead to losses in cultivating these plants. Vectors containing colour and spectral indices were used. These features are obtained through a video sensor on a mobile phone. The kernel method variant of principal components reduces them. Feature vectors are selected using factor analysis, correspondence analysis, and the correlation method. Predictive models have been developed to determine plant pigments. With high accuracy (over 90%), the pigments xanthophyll and chlorophyll A can be predicted.

Monitoring and Control of Particle Matter Concentration’s Traffic-Related Air Pollution Using Low-Cost Mobile Sensors at Six Intersections in Yaoundé, Cameroon

Particle pollution has a major influence on public health in Central African cities. Measuring the levels of pollution to which populations are exposed is difficult because only a few African countries have an air quality monitoring network in place. Yet, given the specific anthropogenic sources prevalent in African countries, as well as the predicted increase in their emissions in the next few years if no laws are implemented, solutions must be developed. The methodology utilised in this study allows diverse research teams to estimate the population's exposure levels to urban particle pollution at a lower cost. In this investigation, we collected data concentrations using Air Master. The spatial mapping of pollutants shows that the Mvog-Mbi crossroad and the Education crossroad are the most polluted points among the six measurement points, with PM2.5 concentrations around 145–170 µg.m-3. Regarding PM10 and PM1, the Mvog-Mbi crossroads is the point where concentrations are highest, fluctuating respectively between 150 and 180 µg.m-3, 41 and 48 µg.m-3. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi Crossroads, Education Crossroads, Poste Centrale, Hilton). These are the high-risk zones for those with respiratory difficulties. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi crossroads, education crossroads, Poste Centrale, Hilton). Regardless of particle size, these pollutants in air readings are greater and beyond the World Health Organization's recommended values. Furthermore, this investigation provides critical information on Yaoundé pollution levels, which can be a major source of disease in the city.

Modelling calibration uncertainty in networks of environmental sensors

Abstract Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.

Nanobiosensing Based on Electro-Optically Modulated Technology.

At the nanoscale, metals exhibit special electrochemical and optical properties, which play an important role in nanobiosensing. In particular, surface plasmon resonance (SPR) based on precious metal nanoparticles, as a kind of tag-free biosensor technology, has brought high sensitivity, high reliability, and convenient operation to sensor detection. By applying an electrochemical excitation signal to the nanoplasma device, modulating its surface electron density, and realizing electrochemical coupling SPR, it can effectively complete the joint transmission of electrical and optical signals, increase the resonance shift of the spectrum, and further improve the sensitivity of the designed biosensor. In addition, smartphones are playing an increasingly important role in portable mobile sensor detection systems. These systems typically connect sensing devices to smartphones to perceive different types of information, from optical signals to electrochemical signals, providing ideas for the portability and low-cost design of these sensing systems. Among them, electrochemiluminescence (ECL), as a special electrochemically coupled optical technology, has good application prospects in mobile sensing detection due to its strong anti-interference ability, which is not affected by background light. In this review, the SPR is introduced using nanoparticles, and its response process is analyzed theoretically. Then, the mechanism and sensing application of electrochemistry coupled with SPR and ECL are emphatically introduced. Finally, it extends to the relevant research on electrochemically coupled optical sensing on mobile detection platforms.