Wireless Sensor Network For Monitoring Research Articles

Integration of Autonomous Wireless Sensor Networks in Academic School Gardens.

In this work, the combination of capabilities provided by Wireless Sensor Networks (WSN) with parameter observation in a school garden is employed in order to provide an environment for school garden integration as a complementary educational activity in primary schools. Wireless transceivers with energy harvesting capabilities are employed in order to provide autonomous system operation, combined with an ad-hoc implemented application called MySchoolGardenApp, based on a modular software architecture. The system enables direct parameter observation, data analysis and processing capabilities, which can be employed by students in a cloud based platform. Providing remote data access allows the adaptation of content to specific classroom/homework needs. The proposed monitoring WSN has been deployed in an orchard located in the schoolyard of a primary school, which has been built with EnOcean’s energy harvesting modules, providing an optimized node device as well network layout. For the assessment of the wireless link quality and the deployment of the modules, especially the central module which needs to receive directly the signals of all the sensor modules, simulation results obtained by an in-house developed 3D Ray Launching deterministic method have been used, providing coverage/capacity estimations applicable to the specific school environment case. Preliminary trials with MySchoolGardenApp have been performed, showing the feasibility of the proposed platform as an educational resource in schools, with application in specific natural science course content, development of technological skills and the extension of monitoring capabilities to new context-aware applications.

Industrial IoT Monitoring: Technologies and Architecture Proposal.

Dependability and standardization are essential to the adoption of Wireless Sensor Networks (WSN) in industrial applications. Standards such as ZigBee, WirelessHART, ISA100.11a and WIA-PA are, nowadays, at the basis of the main process-automation technologies. However, despite the success of these standards, management of WSNs is still an open topic, which clearly is an obstacle to dependability. Existing diagnostic tools are mostly application- or problem-specific, and do not support standard-based multi-network monitoring. This paper proposes a WSN monitoring architecture for process-automation technologies that addresses the mentioned limitations. Specifically, the architecture has low impact on sensor node resources, uses network metrics already available in industrial standards, and takes advantage of widely used management standards to share the monitoring information. The proposed architecture was validated through prototyping, and the obtained performance results are presented and discussed in the final part of the paper. In addition to proposing a monitoring architecture, the paper provides an in-depth insight into metrics, techniques, management protocols, and standards applicable to industrial WSNs.

Assessing the variability and uncertainty of two-flux FAPAR measurements in a conifer-dominated forest

The estimation of the Fraction of Absorbed Active Radiation (FAPAR) and its uncertainties is key for understanding global carbon balances. This work investigates the variability and associated uncertainties of in-situ two-flux FAPAR observations attributed to changes in phenological and meteorological conditions. Specifically, we assessed influences of illumination conditions with solar zenith angle and the ratio of diffuse-to-total incident radiation, wind speed, leaf color and snow coverage on the variability of two-flux FAPAR. We assumed FAPAR acquired under diffuse light conditions to be closest to “true” FAPAR as it is not influenced by the solar zenith angle. To reveal the uncertainty of the two-flux FAPAR measurements, we investigated the difference (Δtwo-flux) between FAPAR acquired under diffuse light conditions and two-flux FAPAR acquired during a certain environmental condition (e.g. large solar zenith angle). A positive (negative) value obtained from this difference was interpreted as an indication for an underestimation (overestimation) of “true” FAPAR by the two-flux FAPAR estimate, as found in previously investigations with canopy radiative transfer models (RTM).Therefore, permanent PAR measurements were carried out 2015–2017 in a sub-alpine, spruce-dominated forest in Southern Germany using a Wireless Sensor Network (WSN). FAPAR observations exhibited considerable seasonal variability (0.89 to 0.99 ± 0.03) despite the dominance of evergreen spruces. The in-situ observations confirm significant overestimation of FAPAR by up to 0.06 under solar zenith angles above 60° and by up to 0.05 during the presence of colored autumn leaves, similarly to the results obtained from previous studies with canopy RTMs. Additionally, our results indicate an effect of wind speed which we consider crucial at sites where high wind speeds occur more frequently. Overall, this study shows the potential of permanent WSN monitoring activities to ensure multi-year FAPAR observations with associated uncertainty information that are demanded to validate satellite-derived FAPAR products in forest ecosystems.

An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks.

The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.

WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline Monitoring

<p>One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical<br />wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we<br />present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.</p>

Wireless Sensor Networks for Long-Term Monitoring of Urban Noise.

Noise pollution in urban environments is becoming increasingly common and it has potential to negatively impact people’s health and decrease overall productivity. In order to alleviate these effects, it is important to better quantify noise patterns and levels through data collection and analysis. Wireless sensor networks offer a method for achieving this with a higher level of granularity than traditional handheld devices. In this study, a wireless sensing unit (WSU) was developed that possesses the same functionality as a handheld sound level meter. The WSU is comprised of a microcontroller unit that enables on-board computations, a wireless transceiver that uses Zigbee protocol for data transmission, and an external peripheral board that houses the microphone transducer. The WSU utilizes on-board data processing techniques to monitor noise by computing equivalent continuous sound levels, LeqT, which effectively minimizes data transmission and increases the overall longevity of the node. Strategies are also employed to ensure real-time functionality is maintained on the sensing unit, with a focus on preventing bottlenecks between data acquisition, data processing, and wireless transmission. Four units were deployed in two weeks field validation test and were shown to be capable of monitoring noise for extended periods of time.

Damage identification in aircraft structures with self‐powered sensing technology: A machine learning approach

Progress in self-powered wireless sensor networks for structural health monitoring (SHM) have motivated the development of power-efficient data communication protocols. One such approach is the energy-aware pulse switching architecture, which employs ultrasonic pulses to transmit binary data through the material substrate. However, this technology creates time delays on the generated data due to the power budgets demanded for sensing and communication. The nature of data collected from such protocol thus requires the development of new analysis and interpretation methods. This study presents a robust damage identification strategy for aircraft structures, within the context of data-driven SHM, using discrete time-delayed binary data. A novel machine learning framework integrating low-rank matrix completion, pattern recognition, k-nearest neighbor, and a data fusion model was developed for damage identification. Performance and accuracy of the proposed data-driven SHM strategy was investigated and tested for an aircraft horizontal stabilizer wing. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The reliability of the proposed strategy with noise-polluted data was also validated. Further, the effect of variations in harvested energy on the performance of the approach was investigated. Results demonstrate that the developed machine learning framework can effectively detect the presence and location of damage based on time-delayed binary data from a self-powered sensor network.

Characterizing unpredictable patterns in Wireless Sensor Network data

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge.This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology.The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning.

Mathematical Tools for Optimization of Energy Consumption in Wireless Sensor Networks

In the design and development of Wireless Sensor Networks (WSNs), one of the main challenges is to achieve maximal battery life-time, because of the constrained energy resources. The application of WSNs for real time monitoring contributes to minimization of potential production risks, emerging mainly from environmental influences and human actions. In the paper mathematical tools for optimization of the energy consumption of wireless sensor nodes while collecting distributed data in environmental parameters monitoring will be presented. In this contribution the stochastic optimization method-genetic algorithm is used to minimize the energy consumption of the wireless sensor nodes depending on the frequency of the transmitted data and the period of the transmission process. The optimization is conducted in cases of different scenarios: while the frequency of the transmitted data as well as the period of transmission of all the active components in a sensor node is increasing.

Integrating wireless sensing and decision support technologies for real-time farmland monitoring and support for effective decision making

This paper presents the design of a (i) soil sensing and monitoring wireless sensor network (S2M-WSN) that can sense and monitor farmland with distributed and networked motes equipped with multiple soil sensors and (ii) a prototype decision support system (DSS) that analyses the soil data received from S2M-WSN using statistical methods, stored rules and knowledge base (KB) and auto-transmits alerts on the mobile phones of end-users. Both S2M-WSN and the DSS are integrated through (iii) predefined data frame format and interface protocol. The developed system has been deployed both in the laboratory and real crop conditions for verification, validation and performance evaluation. The system is evaluated by considering various performance metrics such as ambiguities associated with data, data packet delivery rates, support delivery performance ratio, system response time, and resultant plant growth. The developed system empowers the end users with assistance in on-field decision making through relevant advisories and alerts at right time on their mobile phones. The authors believe that the developed system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.

End-to-End Data Delivery Reliability Model for Estimating and Optimizing the Link Quality of Industrial WSNs

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters. Note to Practitioners —The goal of this paper is to improve the estimation and optimization of the network-level reliability performance of industrial wireless sensor networks (WSNs) to satisfy their strict control requirements. In harsh industrial application scenarios, many factors affect radio link quality, such as RF transmit power, communication distance, and random background noise. Existing link quality estimation approaches mainly focus on providing a smoothed estimation of radio link quality without any solution for optimizing communication reliability to satisfy certain requirements. This paper suggests a new approach in which WSN nodes are used to measure and estimate the parameters of the scenario in which the nodes will be deployed and then to estimate and optimize the worst case reliability (which is a lower-bound value rather than a smoothed value) to ensure that the network is qualified. Through a real-world case study, we demonstrate how to estimate the lower bound on reliability and how to optimize the reliability by computing the maximum deployment distance between nodes as an example. The experiments suggest that this approach is feasible.

Applying ZigBee wireless sensor and control network for bridge safety monitoring

Internet technologies bring methods to help bridge safety management, to collect data or monitor conditions in real time, and to comprehensively record or analyze the collected data of on-site conditions in real time. In this study, the wireless sensor networks and smart building technologies are adopted to help the bridge safety information transmission and management. The study proposed a bridge safety–monitoring system conceptual framework by applying the ZigBee wireless sensor and control technology. The conceptual framework demonstrated by a prototype includes four major subsystems: (1) monitoring units; (2) photovoltaic units; (3) wireless communication system; and (4) bridge safety–monitoring server system. This system can monitor and analyze in real time the conditions of a bridge and its environment, including the waters levels nearby, pipelines, air, and other safety conditions. The detected data and images are transmitted to the server and database for users to have real-time monitoring of the bridge conditions via mobile telecommunication devices.

Deep Mining of Redundant Data in Wireless Sensor Network Based on Genetic Algorithm

Mining effective data from wireless sensor network node data is one of the main subjects in studies concerning wireless sensor network data processing. Wireless sensor network data are muli-dimensional and dynamic. Generally, data mining technology cannot satisfy the requirements of wireless sensor network. A large amount of accumulated and redundant wireless sensor network monitoring data reduces the efficiency of data processing. To solve the above problems, this study proposed a data mining algorithm, which integrated rough set algorithm and genetic algorithm to mine redundant data in node network data. The results of the simulated calculation based on MATLAB platform suggested that the identification rate, false accept rate and reject rate of the proposed algorithm were 94.65, 1.753 and 2.331%; compared to network data mining algorithm based on improved genetic algorithm, it has higher efficiency and accuracy in data mining. The algorithm could effectively excavate redundant data in wireless sensor network and optimize the operation environment of wireless sensor network. The application of the rough set and genetic algorithm based data mining algorithm in wireless network has a promising prospect.

Application Research of Wireless Sensor Network in Building Structure Safety Monitoring

In order to ensure the safety of important buildings, the structural safety monitoring of buildings is indispensable. Compared with wired sensor network, wireless sensor network have the advantages of deployment flexibility, lower maintenance cost and higher monitoring accuracy. It has a better application prospect in the safety monitoring application of building structure. This paper studied the application of wireless sensor network in the safety monitoring of buildings by studying the application requirements in the safety monitoring of building structure in important buildings, proposed a routing algorithm to improve the network lifetime, and simulated the routing algorithm. The results show that the algorithm can effectively improve the network lifetime.

Exploiting Correlation for Confident Sensing in Fusion-Based Wireless Sensor Networks

Wireless sensor networks for obstacle detection, environment monitoring, and military surveillance have stringent accuracy requirements for target monitoring and demand long system lifetime. We define meeting such user requirements as confident sensing. In order to achieve confident sensing and energy balance among sensors, it is necessary to cluster sensors properly. But in most traditional approaches, such as sensor-target distance based methods, users’ accuracy requirements on sensor correlation can hardly be fulfilled. Accordingly, we first make quantitative study on relationship between sensor-target distance and sensing accuracy as well as sensor correlation among the heterogeneous or homogeneous sensors. Their impacts on sensor collaboration provide guideline for confident sensing. Then, in order to meet user sensing accuracy requirements and obtain energy balance among sensors, we propose a collaborative sensor selection method named CSdT. Based on sensor-target distance and sensor correlation, CSdT scheme clusters right sensors in a distributed way. Besides, from perspective of mutual information, we explain the role of such correlation and sensor-target distance on confident sensing. Finally, we conduct extensive experiments on real data to validate and evaluate CSdT. Experimental results demonstrate the superior performance of our method over existing solutions in terms of meeting users’ requirements and obtaining energy balance.

Dynamic cluster heads selection and data aggregation for efficient target monitoring and tracking in wireless sensor networks

Due to energy limitation in wireless sensor networks, clustering is an efficient scheme which has been widely used in building practical wireless sensor networks, and various cluster head selection methods have been proposed nowadays. However, less emphasis was placed on the application constraints cluster head selection. In traditional clustering wireless sensor networks, cluster head is always located at the cluster centre and cannot detect an intruding target since the target first transits the border. Moreover, the data sensed from a target are sent by each cluster head through different routings to the sink so that it cannot be aggregated efficiently near the data source. In order to address these problems, this article proposes an efficient target tracking approach, in which the nodes on the edge of a cluster instead of the centred nodes are chosen as cluster heads so that cluster heads can serve as manager and monitoring node. Furthermore, we choose a collecting cluster head to collect the sensed data from the cluster heads around the target to facilitate data aggregation. Hence, the sensed data can be aggregated near to the data source, which avoids the data long-distance transmission and reduces data gathering costs. Moreover, each cluster head has different lifetime in the efficient target tracking approach according to its location and residual energy to balance the energy cost. Experimental results show that efficient target tracking approach outperformed the state-of-the-art approaches by improving the energy consumption as well as prolonging the network lifetime by about 20% as the 20% nodes die.

A need for an integrative security model for semantic stream reasoning systems

State-of-the-art security frameworks have been extensively addressing security issues for web resources, agents and services in the Semantic Web. The provision of Stream Reasoning as a new area spanning Semantic Web and Data Stream Management Systems has eventually opened up new challenges. Namely, their decentralized nature, the metadata descriptions, the number of users, agents, and services, make securing Stream Reasoning systems difficult to handle. Thus, there is an inherent need of developing new security models which will handle security and automate security mechanisms to a more autonomous system that supports complex and dynamic relationships between data, clients and service providers. We plan to validate our approach on a typical application of stream data, on Wireless Sensor Networks (WSNs). In particular, WSNs for water quality monitoring will serve as a case study. The paper describes the initial findings and research plan for building a consistent security model for stream reasoning systems.

Mobile crowd sending in traffic monitoring

Objectives: To propose a technology to sense the environment without using the dedicated architecture for sensing. Methods/Statistical Analysis: Mobile crowd sensing is a new emerging technology in the field of wireless sensor network environment monitoring. Wireless sensor network monitoring enters a new paradigm with mobile crowd sensing. The technology makes the user to participate in the event and user’s mobile device is used to communicate the data in different environment. The sensing network uses the mobiles of user to collect the data. In our application discussed here we are monitoring the traffic in public transport system. We use the public’s opinion to collect data by an application in their mobiles. Through we are able to know the traffic in road, crowd in the buses, and quality of buses. Findings: We also compared this with normal wireless sensor networks used for monitoring the transport in the roads on comparison we find the MCS provides better sensing than normal wireless sensor networks in the case energy management, memory management and performance improvement. Application/Improvements: The technology can be improved or established in train transport and other public place cleanliness monitoring. Keywords: Energy Conservation, MCS, Memory Management, Public Transport, WSN

A comprehensive study for moisture control in cultural heritage using non-destructive techniques

This article describes the use of non- or minimally destructive methods to study damp in San Juan Bautista Church at Talamanca de Jarama in the Spanish province of Madrid. The combination of Electrical Conductivity Meter (EC), Infrared Thermography (IR), RH/T monitoring by means of Data Loggers (DL) and Wireless Sensor Networks (WSN), Electrical Resistivity Tomography (ERT) and Ground-Penetrating Radar (GPR) techniques provided sub-surface and surface information. The respective findings and ranges of observation were inter-related to identify the decay associated with the damp and determine the advantages and drawbacks of each instrumental method. Electrical conductivity meters and infrared thermography deliver rough estimates of the extent of damp on wall surfaces, furnishing qualitative information on the areas where depth studies should be undertaken using techniques such as ERT or GPR or where long-term WSN monitoring sensors should be positioned to determine variability in a given number of points. When the purpose is to distinguish between areas with widely varying moisture contents, ERT is more precise. However, GPR proved to be the most accessible and efficient technique for studying the under-floor profiles.