Sensor Networks For Environmental Monitoring Research Articles

Synergistic Integration of Nanogenerators and Solar Cells: Advanced Hybrid Structures and Applications

AbstractThe rapid growth of global energy consumption and the increasing demand for sustainable and renewable energy sources have urged vast research into harnessing energy from various sources. Among them, the most promising approaches are nanogenerators (NGs) and solar cells (SCs), which independently offer innovative solutions for energy harvesting. This review paper presents a comprehensive analysis of the integration of NGs and SCs, exploring advanced hybrid structures and their diverse applications. First, an overview of the principles and working mechanisms of NGs and SCs is provided for seamless hybrid integrations. Then, various design strategies are discussed, such as piezoelectric and triboelectric NGs with different types of SCs. Finally, a wide range of applications are explored that benefit from the synergistic integration of NGs and SCs, including self‐powered electronics, wearable devices, environmental monitoring, and wireless sensor networks. The potential for these hybrid systems is highlighted to address real‐world energy needs and contribute to developing sustainable and self‐sufficient technologies. In conclusion, this review provides valuable insights into the state‐of‐the‐art developments in NGs and SCs integration, shedding light on advanced hybrid structures and their diverse applications.

Several Categories of Energy Harvested Routing Protocols, Challenges, and Characteristics in WSN: A Review

The routing protocol is a technique for determining the most efficient channel for data transmission. The route selection procedure, which relies on the kind of network, channel conditions, and measurement systems, presents several challenges. Routing is essential in Wireless Sensor Networks (WSNs) for environmental monitoring, traffic monitoring, and other applications. WSNs are small nodes that can sense, interpret data, and communicate wirelessly. Many routing, power control, and data dissemination techniques have been developed specifically for WSNs, where energy efficiency is a crucial design factor.
 On the other hand, the focus has been on energy harvesting and standard routing methods, which can vary depending on the design and network architecture. In a Wireless Sensor Network (WSN), the data collected by the sensor nodes is typically transferred to the base station, which connects the sensor network to other networks (such as the internet), where it is processed and necessary action is taken. WSN has recently been developed to allow various applications, including traffic enforcement building automation, smart warfare, environmental sensing, and many more.WSN integrates several sensors or nodes deployed around a specific node to perform computational processes.

Additively manufactured magic cube platforms for fully integrated wireless sensing nodes for Internet of Things applications

Wireless sensor networks for environmental monitoring are a key feature in developing the Internet of Things. Although there has been much research in developing components for wireless sensing nodes, advances in creating fully integrated sensing nodes is limited. Furthermore, because most sensing nodes that have been developed are intended to perform a fixed task, each new effort to design an integrated sensing node with different functionality must start from scratch. Here we introduce a broadly applicable platform for the development and production of fully integrated wireless sensing nodes. The platform is an additively manufactured cube that has different subsystems occupying separate faces of the 3D structure. While both additively manufactured sensors and cube-shaped wireless sensing nodes have been previously reported, these two approaches have yet to be combined. A key technology that enables this is the use of additively manufactured, nonplanar bent microstrips. This realization offers a “plug-and-play” approach to sensor node design, as the subsystems are considered modular and can be swapped to alter the function of the device. Implementing this concept enables the rapid development and deployment of wireless sensor networks.

Accelerometer intensity vector sensor network for environmental noise monitoring with source direction and location

AIVS (Accelerometer-based Intensity Vector Sensors, /āvs/) represent a new way to measure 3-d sound and are designed to integrate into existing noise monitoring solutions. Standard microphones measure sound pressure, which cannot alone deduce the direction of sound propagation. AIVS is based on the measurement of the velocity of a small parcel of air surrounding a triaxial accelerometer, from which a vector-based representation of sound intensity is calculated. AIVS integrates a MEMS triaxial accelerometer with one MEMS microphone and synchronously measures particle velocity and pressure, resulting in a 3-d intensity vector at each AIVS node. An AIVS network is synchronized to GNSS time and sensors are deployed in groups surrounding and/or within a local measurement site. Low power AIVS nodes are location-aware and estimate azimuth and elevation angles to detected noise sources as a function of frequency. Range to source is computed when noise events are observed from multiple nodes. AIVS nodes are managed by a Raspberry-PI (RPI) sensor hub in a wired CAN-bus supporting distances up to 100 m, or via the Bluetooth Low Energy (BLE) protocol. More widely separated nodes are joined through WWLAN technologies via the local RPI hub.

Implementation of a WSN for Environmental Monitoring: From the Base Station to the Small Sensor Node.

In this paper, the implementation of a Wireless Sensor Network (WSN) for environmental monitoring (EM) is presented. It includes the design, implementation and experimental characterization of a multi-sector base station (BS) antenna composed of several microstrip Quasi-Yagi elements and the implementation and experimental characterization of a reduced form factor antenna for the sensor nodes (SN). Subsequently, it reports the implementation of a WSN based on Lopy4 transceivers, using the developed BS and SN antennas. Finally, experimental results obtained on the field to evaluate the performance of the network in terms of maximum coverage distance and coverage area are presented. According to the field tests, the connectivity between the sensor nodes and the developed WSN base station is confirmed at distances above 3.5 km and for all the antenna sectors of the multi-sector BS attaining a 360 of field of view.

Design and implementation of wireless sensor network for environmental monitoring

Wireless Sensor Networks (WSN) are being developed in the agriculture and weather station sectors to monitor and manage a number of objects. Sensor network hardware platforms are low-power embedded systems featuring a variety of sensors, such as on-board sensors and analogue I/O ports for adding sensors. The design of genuine environmental monitoring and communication systems is greatly aided by the creation of a WSN. A WSN is a collection of sensors that are geographically spread and specialized for monitoring and recording environmental variables and storing the findings in a central location. Limited power consumption as sensor nodes works on batteries, Nodes are mobile opposed to conventional wires, Usage in harsh environment, Nodes can be deployed in any fashion over an area, Easy to use and multi-layer design operation these are the main characteristics of WSN. Limits imposed on the energy storage device by their applications and deployment settings are less relevant to WSN environment-monitoring applications than controlling power consumption and optimizing data transmission. A WSN is used to record real-time traffic and environmental data in order to develop descriptive and predictive models for determining the optimum routes to reduce traffic congestion and, as a result, urban pollution. C

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks.

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

Energy-efficient collection scheme based on compressive sensing in underwater wireless sensor networks for environment monitoring over fading channels

Considering the energy-limited and fading channels of large underwater wireless sensor networks (UWSNs) in long-term environment monitoring, an energy-efficient collection scheme based on compressive sensing (CS) in UWSNs for environment monitoring over fading channels is proposed. In this paper, a CS-based UWSNs data collection model is established by exploiting the spatial sparsity of underwater environment data to reduce the number of sensor nodes required. By considering the impact of channel fading on instantaneous power, a packet transmission strategy is deduced to ensure the successful reception of given number of packets. Furthermore, a CS-based energy-efficient collection scheme is proposed based on the model and the strategy to realize an efficient monitoring of the target field and reduce the energy consumption of UWSNs. Performance analysis is conducted and real data example are provided to illustrate the validity of the proposed scheme.

Collaborative Allocation and Optimization of Path Planning for Static and Mobile Sensors in Hybrid Sensor Networks for Environment Monitoring and Anomaly Search.

In this study, a novel collaborative method is developed to optimize hybrid sensor networks (HSN) for environmental monitoring and anomaly search tasks. A weighted Gaussian coverage method hs been designed for static sensor allocation, and the Active Monitoring and Anomaly Search System method is adapted to mobile sensor path planning. To validate the network performance, a simulation environment has been developed for fire search and detection with dynamic temperature field and non-uniform fire probability distribution. The performance metrics adopted are the detection time lag, source localization uncertainty, and state estimation error. Computational experiments are conducted to evaluate the performance of HSNs. The results demonstrate that the optimal collaborative deployment strategy allocates static sensors at high-risk locations and directs mobile sensors to patrol the remaining low-risk areas. The results also identify the conditions under which HSNs significantly outperform either only static or only mobile sensor networks in terms of the monitoring performance metrics.

Sensor network data denoising via recursive graph median filters

In wireless sensor networks (WSN) for environmental monitoring, the sensor nodes typically have limited computational and communication resources. Furthermore, the sensors often operate in a harsh environment, and the measurements can be subjected to significant levels of noise. In this work, with these considerations in mind, we propose an efficient method to denoise the sensor data. Using concepts from graph signal processing, the WSN is first modelled using an extended graph. The time-vertex graph jointly models the correlations between neighbouring sensor nodes and across the time dimension. A recursive graph median filter is developed that can be highly localized, and can be implemented with distributed processing. The filter is applied to the denoising of data that is subjected, simultaneously, to Gaussian noise and impulsive noise. Extensive experimental results, using three real-world measurement datasets, will demonstrate that the recursive filter significantly outperforms the equivalent linear filter and nonrecursive median filter, at high noise levels.

Secure Data Exchange Based on Wireless Sensor Network for Environmental Monitoring Using Dynamical Attributed Based Encryption

The basics Internet of things based on Wireless Sensor Network (WSN) has grown rapidly in Industrial Revolution 4.0. Many researchers use the Wireless Sensor Network technology for obtaining the sensor data and stored in the Data Center. The collected data through WSN from an environment was sent to a Data Center. In the Data Center, all the sensor data can be accessed by everyone using their devices. In this case, the user can get the data with a smartphone, laptop, and personal computer through the HTTP Protocol. Data Center without protection from illegal access is extremely dangerous. All the sensor data stored in the data center can be intercepted, tracked, and even changed by a user without access. There is required a security mechanism for protecting all of the data stored in the Data Center. Ciphertext Policy Attributed-Based Encryption can be deployed as a security mechanism for protecting all the sensor data in the Environmental Monitoring. The security mechanism protected Data Center with encryption, decryption, verification, and revocation access for all the users. The only user with the access right can get the sensor data from environmental monitoring. Our security mechanism offers revocation and verification with a dynamic attribute for each user with a lower computational time. We used the CP-ABE with Dynamical attribute and timestamp digital signature based on Elliptic Curve Digital Signature Algorithm (ECDSA) 384 bits.

Research on a floating thermoelectric power generator for use in wetland monitoring.

A floating power generation device is designed and fabricated to overcome the power supply limitations of wireless sensor networks for environmental monitoring. Once there is a temperature difference between the upper surface exposed to sunlight and the lower surface in the water, the device is capable of generating power while floating in the wetland environment. Fresnel lenses were applied to concentrate solar irradiation on a selective absorbing coat. Meanwhile two vertical axis rotors were used to cool the cold side of the thermoelectric power generator by catching the breeze. The effects of solar irradiation, temperature distribution, load resistance, wind speed, the maximum power and the electrical efficiency of the thermoelectric power generator were analyzed. When subjected to solar irradiation of 896.38 W/m2, the device generated a potential difference of 381.03 mV and a power output of 8.86 mW via thermoelectric generation. In addition, compared with the system without wind, the output power was increased by approximately 10.96% in our system. The low power wireless networks, used in wetland environments, could be operated by the thermoelectric power generated by the floating device. Besides, this system offers powering solution for self-power miniature devices that are applied in aqueous environment.

Cluster based wireless sensor network for forests environmental monitoring

Cluster based wireless sensor network for forests environmental monitoring

Iot Wearable Sensor Network for Environmental Monitoring

We discover that IoT acceptance is widespread: on numerous continents, over half of households actually have at the very least one IoT unit. Unit styles and supplier appeal differ considerably all over areas. The system is actually intended for enhancing protection in the exterior office. The recommended unit features a wearable body system region network to pick up customer data as well as a low-power wide-area network to connect the WBAN with the Internet. The wearable picking up units in the WBAN is exerted to assess the ecological troubles around the subject matter utilizing a Safe Node as well as additionally track the crucial indications of the target making use of a Health and wellness node.

A Novel Intrusion Detection System in WSN using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. A new method of intrusion detection using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm (HNF-ACA) is proposed in this study, which has been able to map the network status directly into the sensor monitoring data received by base station, accordingly that base station can sense the abnormal changes in network.The hybridized Sugeno-Mamdani based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. The Modified Mutation Based Ant Colony Algorithm technique improves the accuracy of determining the membership values of input trust values of each node in fuzzy filters. To end, the proposed method was tested on the WSN simulation and the results showed that the intrusion detection method in this work can effectively recognise whether the abnormal data came from a network attack or just a noise than the existing methods.

Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome-based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortion-rate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

New Techniques for Sizing Solar Photovoltaic Panels for Environment Monitoring Sensor Nodes

The development of perpetually powered sensor networks for environment monitoring to avoid periodic battery replacement and to ensure the network never goes offline due to power is one of the primary goals in sensor network design. In many environment-monitoring applications, the sensor network is internet-connected, making the energy budget high because data must be transmitted regularly to a server through an uplink device. Determining the optimal solar panel size that will deliver sufficient energy to the sensor network in a given period is therefore of primary importance. The traditional technique of sizing solar photovoltaic (PV) panels is based on balancing the solar panel power rating and expected hours of radiation in a given area with the load wattage and hours of use. However, factors like the azimuth and tilt angles of alignment, operating temperature, dust accumulation, intermittent sunshine and seasonal effects influencing the duration of maximum radiation in a day all reduce the expected power output and cause this technique to greatly underestimate the required solar panel size. The majority of these factors are outside the scope of human control and must be therefore be budgeted for using an error factor. Determining of the magnitude of the error factor to use is crucial to prevent not only undersizing the panel, but also to prevent oversizing which will increase the cost of operationalizing the sensor network. But modeling error factors when there are many parameters to consider is not trivial. Equally importantly, the concept of microclimate may cause any two nodes of similar specifications to have very different power performance when located in the same climatological zone. There is then a need to change the solar panel sizing philosophy for these systems. This paper proposed the use of actual observed solar radiation and battery state of charge data in a realistic WSN-based automatic weather station in an outdoor uncontrolled environment. We then develop two mathematical models that can be used to determine the required minimum solar PV wattage that will ensure that the battery stays above a given threshold given the weather patterns of the area. The predicted and observed battery state of charge values have correlations of 0.844 and 0.935 and exhibit Root Mean Square Errors of 9.2% and 1.7% for the discrete calculus model and the transfer function estimation (TFE) model respectively. The results show that the models perform very well in state of charge prediction and subsequent determination of ideal solar panel rating for sensor networks used in environment monitoring applications.

An Energy-Efficient Dual Prediction Scheme Using LMS Filter and LSTM in Wireless Sensor Networks for Environment Monitoring

Environmental monitoring is a practical application where a wireless sensor network (WSN) may be utilized effectively. However, the energy consumption issues have become a major concern in using a WSN, particularly in remote locations without readily accessible electrical power supply. In general, the activities of data transmission among sensor nodes and the gateway (GW) can be a significant fraction of the total energy consumption within a WSN. Hence, reducing the number and the duration of transmissions as much as possible while maintaining a high level of data accuracy can be an effective strategy for saving energy. To achieve this objective, a least mean square (LMS) filter is used for a dual prediction scheme (DPS), in this paper. The DPS is data quality-based, allowing both the sensor nodes and the GW to predict the data simultaneously. Only when the error between the predicted data and the real sensed data exceeds a predefined threshold, the sensor nodes will send the sensed data to the GW/another node and consequently will update the coefficients of the filter. It is observed that, with this scheme, the total number of transmissions and their overall duration can be effectively reduced, and therefore, further energy savings can be realized. With the developed methodologies, at least 62.3% of the total energy for data transmission could be saved while achieving a 93.1% prediction accuracy.

Introducing Nature 4.0: A sensor network for environmental monitoring in the Marburg Open Forest

Successful conservation strategies require frequent observations and assessments of the landscape. Although expert surveys provide a great level of detail, the trade-off is the limited spatial coverage and repetition with which they are executed. Remote sensing technology can partially resolve these issues; nevertheless, it still requires experts’ experience to create conservation planning and reaction options. Nature 4.0 seeks to address these shortcomings by developing a prototype of a modular environmental monitoring system for high-resolution observation of species, habitats, and processes. The project combines expert surveys by nature conservationists, remote sensing, and a network of environmental sensors, which are integrated into stationary units as well as attached to unmanned aerial vehicles, rovers, or animals. By utilizing powerful data integration and analysis methods, Nature 4.0 will enable researchers to effectively observe landscapes through a set of diverse lenses. Time series data from the project will also inform the development of early warning indicators. Following the open-source principle, as much of the project as possible will be made publicly available, including, for instance, schematics for sensor units, algorithms for data integration or information on species occurrence. In summary, Nature 4.0 will establish new methods and protocols in the field of comprehensive environmental monitoring by combining traditional sampling, remote sensing, and automated measurement stations. The prototype system is being developed in the Marburg Open Forest, an open research, education, and development platform for environmental monitoring methods. The Marburg Open Forest brings a cross-disciplinary group of scientists together with nature conservation experts from the private sector and the state government, as well as local schools and private citizens to collaborate and bridge the gap between basic and applied environmental research. After one year, we will present the results of the initial phase and share our experience with developing Nature 4.0.

Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities

Nowadays, more than half of the world’s population lives in urban areas. Since this proportion is expected to keep rising, the sustainable development of cities is of paramount importance to guarantee the quality of life of their inhabitants. Environmental noise is one of the main concerns that has to be addressed, due to its negative impact on the health of people. Different national and international noise directives and legislations have been defined during the past decades, which local authorities must comply with involving noise mapping, action plans, policing, and public awareness, among others. To this aim, a recent change in the paradigm for environmental noise monitoring has been driven by the rise of Internet of Things technology within smart cities through the design and development of wireless acoustic sensor networks (WASNs). This work reviews the most relevant WASN-based approaches developed to date focused on environmental noise monitoring. The proposals have moved from networks composed of high-accuracy commercial devices to the those integrated by ad hoc low-cost acoustic sensors, sometimes designed as hybrid networks with low and high computational capacity nodes. After describing the main characteristics of recent WASN-based projects, the paper also discusses several open challenges, such as the development of acoustic signal processing techniques to identify noise events, to allow the reliable and pervasive deployment of WASNs in urban areas together with some potential future applications.