Low-cost Wireless Sensor Networks Research Articles

Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor

The use of sensor networks in monitoring applications has increased; they are useful in security, environmental, and health applications, among others. These networks usually transmit data through short-range stations, which makes them attractive for incorporation into applications and devices for use in places without access to satellite or mobile signals, for example, forests, seas, and jungles. To this end, unmanned aerial vehicles (UAVs) have attractive characteristics for data collection and transmission in remote areas without infrastructure. Integrating systems based on wireless sensors and UAVs seems to be an economical and easy-to-use solution. However, the main difficulty is the amount of data sent, which affects the communication time and even the flight status of the UAV. Additionally, factors such as the UAV model and the hardware used for these tasks must be considered. Based on those difficulties mentioned, this paper proposes a system based on long-range (LoRa) technology. We present a low-cost wireless sensor network that is flexible, easy to deploy, and capable of collecting/sending data via LoRa transceivers. The readings obtained are packaged and sent to a UAV. The UAV performs predefined flights at a constant height of 30 m and with a direct line-of-sight (LoS) to the stations, during which it collects information from two data stations, concluding that it is possible to carry out a correct data transmission with a flight speed of 10 m/s and a transmission radius of 690 m for a group of three packages confirmed by 20 messages each. Thus, it is possible to collect data from routes of up to 8 km for each battery charge, considering the return of the UAV.

Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system

It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative for environmentally sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has emerged as a promising solution. Smart Agriculture can significantly bolster agricultural development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging the insights from this data can facilitate informed decision-making regarding water management, thereby fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather prediction services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study provides a clear roadmap that can be readily adapted for similar research endeavors.© 2023 Elsevier Inc. All rights reserved.

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop.

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.

A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN.

Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies.

A Low-Cost Wireless Sensor Network for Barn Climate and Emission Monitoring—Intermediate Results

A barn’s climate is vital for animal welfare and emissions control, including greenhouse gases like methane (CH4) and carbon dioxide (CO2) and environmental gases like ammonia (NH3). The goal of this study was to investigate a developed low-cost online tool for monitoring barn climate and air pollutant emissions (OTICE) in naturally ventilated barns. OTICE employed a wireless sensor network with low-cost sensors for gases and climate variables, allowing scalable use across multiple barns. We evaluated the sensors for CO2, NH3, and CH4 for accuracy, both in controlled lab conditions and in a dairy barn in Germany, where measurements were carried out continuously for a duration of 12 days. For the averaged concentration levels over the measurement period, the low-cost sensors agreed well with the reference system, with relative deviations lower than 7% for all three gases, with maximum peak deviations up to 32% for CO2, 67% for NH3, and 65% for CH4, with strong Spearman correlations for CO2 and NH3 (ρCO2 = 0.8, ρNH3 = 0.68) and a rather weak correlation for CH4 with ρCH4 = 0.24. Further calibration and stability investigations are required, especially for CH4 sensing. However, the overall good results for NH3 and especially CO2 measurements indicate a huge potential of the low-cost system as a valuable tool for monitoring relative NH3 emission levels and the measurement of air exchange rates in naturally ventilated barns.

Two-Level Sensor Self-Calibration Based on Interpolation and Autoregression for Low-Cost Wireless Sensor Networks

Two-Level Sensor Self-Calibration Based on Interpolation and Autoregression for Low-Cost Wireless Sensor Networks

BROACHING UNCOUTH WATER LEVEL SNAG IN UNDERGROUND AGRICULTURE FIELD THROUGH WIRELESS SENSORS

The major problem faced by the agriculture industry is the growth of seeds at optimal fertility. Such uncouth fertility occurs when either no water reaches under soil seed or some obstacles prevent it. This issue degrades agriculture growth at an aggravation level and consequently barren the land. A wireless sensor-based communication model has been developed to notice the accurate status of the water level reaching seeds of various crops such as wheat, maize, barley, and cotton with soil types Loamy, Silty clay, and Black soil. In the proposed model, "Broadening Uncouth Water Level Snag in Underground Agriculture Field with Wireless Sensors" (BUWLN), the water level would be measured in real time and the data would be transmitted to a base station accordingly. The 8 sensor nodes and a base station would be deployed accompanied by an Arduino package in an area of 90 x 30 square feet to monitor water levels efficiently. The study will keep track of water levels, and the farmer will be informed of the water level so they may manage water resources more effectively. In addition, farmers will also be aware of the field if water is distributed uniformly so that the crops yield increases. The performance of the proposed BUWLN model has been compared with Remote water-level monitoring system (RWMS), Low cost wireless sensor network (LCSN), and Ensemble groundwater level prediction (E-GWLP) by conducting simulation using PROTEUS. result showed that the proposed BUWLN model has enough potential for future growth.

Internet of Things experimental platform for real-time water monitoring: a case study of the Araranguá River estuary

The Internet of Things (IoT) is a technology that can be employed to monitor several sectors, mostly precision agriculture, where environmental data can be used to better manage resources, reduce costs, and improve the use of natural resources. This work proposes the application of cloud-based IoT for monitoring irrigation water in rice fields. Southern Brazil was chosen as a case study because it suffers from water pollution problems due to urban activities, coal mining, and the inflow of saline water into the Araranguá River estuary. All these factors can compromise the water quality for irrigation. The presence of salt in irrigation water can compromise rice growth, making its monitoring essential for successful cultivation. Currently, the rice farmer needs to travel to the riverbank to assess the water quality using a manual sensor. This procedure demands time and resources and is not always efficient. The technology helps indicate whether the water is of quality to irrigate crops, helping the farmers decide whether to use that water to irrigate their fields. The solution is based on low-cost wireless sensor network devices, with subsequent transmission of data from the gateway using the network mobile phone cloud. Data collected were also accessed by the smartphone through a mobile application. This system was implemented for testing in the Araranguá River estuary, southern Santa Catarina, Brazil. Also, it appears as an option for farmers who need to monitor the quality of the water channel of the crop, thus avoiding the loss and reduction in crop productivity because the system can be continuously monitored and show notifications to the user. The highlights of this work were prototyping a complete solution to help the farmer using a smartphone application.

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient’s state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient’s home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person’s sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network.

Forest Fire Detection System based on Low-Cost Wireless Sensor Network and Internet of Things

Forest fires are one of the most devastating natural disasters that can have a significant impact on the environment, economy, and human lives. Early detection and prompt response are crucial to minimize the damage caused by forest fires. In recent years, Wireless Sensor Networks (WSN) and Internet of Things (IoT) technologies have emerged as promising solutions for forest fire detection due to their low-cost and efficient monitoring capabilities. This paper proposes a low-cost forest fire detection system based on WSN and IoT. The system uses a network of sensor nodes that are strategically placed in the forest to monitor environmental conditions such as temperature, humidity, and smoke. The sensor data is transmitted to a central server, where advanced algorithms are used to detect and predict the occurrence of forest fires. The system provides real-time alerts to forest authorities and users using a mobile application that shows the fire maps and the current updates. The proposed system has been evaluated using based on experiments, and the results show that it can effectively detect forest fires with high accuracy, low false alarms, and low cost. This system has the potential to provide an efficient and cost-effective solution for forest fire detection and can play a vital role in protecting the environment and saving lives.

Smart platform based on IoT and WSN for monitoring and control of a greenhouse in the context of precision agriculture

The rapid evolution of communication technology has spurred a revolution in many domains, which makes life more comfortable and convenient. In order to meet the surge in food demand due to the worldwide growing population, reduce farmers' efforts, and benefit from these technologies in agriculture, an intelligent platform based on IoT (Internet of Things) is proposed in this paper for monitoring and controlling greenhouse climate and irrigation. The system used a low-cost wireless sensors network (WSN) based on radio frequency (RF) communication for collecting and sending the greenhouse data (temperature, humidity, and soil moisture) to a processing unit based on Raspberry Pi. A fusion system based on fuzzy logic controller (FLC) was used to combine the collected data and to supply smart and optimal decisions to manage the internal climate and irrigation. The user can remotely monitor the greenhouse using a Human Machine Interface (HMI) developed under Node-RED server of IBM. In addition, the system allowed storing the environment data in a MySQL database to be exploited for control, monitoring, or history storage. The test results showed the effectiveness and success of the proposed platform.

MODEL OF A CHAOTIC ULTRA-WIDEBAND INFORMATION TRANSMISSION SYSTEM FOR WIRELESS SENSOR NETWORKS

Distributer measuring systems, consisting of interconnected wireless sensor nodes with autonomous supply, have plenty of applications in different areas of engineering end technology. Therefore, wireless sensor network design and optimization is a vital problem in informational technologies and computer engineering. Regardless of particular application and architecture, a wireless sensor network consists of several small sensor nodes somehow distributed in an area of inspection. Such a design ensures a distributed parameter measuring, according to which, each node performs preprocessing procedures for the data, which are to be aggregated by a so-called sink node and sent via other networks, e.g., wired or wireless local area network. For healthcare solutions, it is important to ensure a stable connection with the network and server back-end to provide a high-level analysis based on predicting methods of machine learning algorithms. This paper describes an application of an ultra-wideband communication model based on chaos synchronization as a low-power and efficient alternative solution for building wireless sensor networks. Thus, using chaotic signals as information carriers in wireless communication has several advantages, including a wide smooth spectrum, high information capability, cybersecurity, and low power consumption. The synchronization problem is one of the most vital tasks to be solved to design a chaos application for ultra-wide-band communication. Being well-studied for periodic signals, modern synchronization theory contains plenty of solutions for classical telecommunication and radio engineering systems, however, it is not developed enough for chaotic systems. Hence, a one-directional dissipative synchronization between two Chen systems is studied in the first section. The second section is devoted to the computer simulation of the model, described in the previous section. All the models built and the simulations performed have been done using MATLAB/Simulink software. The negative impact of channel noise and inequality of system parameters is considered. The possible way how to improve technical characteristics is also provided. Proposed models are to be used to design and develop low-cost wireless sensor networks for multi-channel healthcare solutions.

Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia

Unsustainable use of water resources and climate change will exacerbate the existing tensions surrounding resources, especially in the Mediterranean context. Despite investments in costly modern equipment, the performance of irrigated agriculture remains below expectations, notably because of the lack of available water data and the limited use of decision support tools. Although a variety of soil moisture sensors are available on the market, they are not widely used by the agricultural community because of their high cost and complexity. Access to information at an unprecedented level, via easily accessible low-cost and low-tech sensors, may be a major lever for improved identification of achievable gains in performance, and to guide actors toward efficient water management. To explore this hypothesis, an open source wireless soil moisture sensor, low-energy and economically and technically accessible, was developed. The tool was designed according to water users’ requirements and applied to a Tunisian irrigation scheme subject to major water use efficiency issues. The functioning of the wireless sensor network was tested on pilot plots over a growing season and compared with commercial sensors. A single parameter calibration can be performed in either the laboratory or the field. This low-cost sensor can be used for real-time irrigation monitoring and as a decision-making tool for water management.

Rule-Driven Forwarding for Resilient WSN Infrastructures.

We present a simple, robust, ad hoc forwarding scheme for small-footprint, low-cost wireless sensor networks (WSNs) and discuss some of its features from the viewpoint of resilience. The class of applications shaping the resilience requirements for our discussion covers temporary, and possibly disposable deployments of potentially massive populations of sensing nodes to assist in the management of emergency missions, including the management of the effects from natural or man-made disasters. Our goal is to make the network resistant to failures of any of its fragments, which may result from the network's intended modus operandi or from intentional malicious attempts at crippling its function, while keeping the cost and footprint of the devices at the absolute minimum.

Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network.

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research.

Adaptive Trust-Based Framework for Securing and Reducing Cost in Low-Cost 6LoWPAN Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are the core of the Internet of Things (IoT) technology, as they will be used in various applications in the near future. The issue of security and power consumption is still one of the most important challenges facing this type of network. 6LoWPAN protocol was developed to meet these challenges in networks with limited power and resources. The 6LoWPAN uses a hierarchical topology and the traditional method of encryption and key management, keeping power consumption levels high. Therefore, in this paper, a technique has been developed that helps in balancing security and energy consumption by exploiting the Trust technique between low-cost WSN nodes called Trust-Cluster Head (Trust-CH). Trust between nodes is built by monitoring the behavior of packet transmission, the number of repetitions and the level of security. The Trust-CH model provides a dynamic multi-level encryption system that depends on the level of Trust between WSN nodes. It also proposes a dynamic clustering system based on the absolute-trust level in the mobile node environment to minimize power consumption. Along with a set of performance metrics (i.e., power consumption and network lifetime), the Cooja simulator was used to evaluate the Trust-CH model. The results were compared to a static symmetric encryption model together with various models from previous studies. It has been proven that the proposed model increases the network lifetime by 40% compared to previous studies, as well as saves as much as 28% power consumption in the case of using a static encryption model. While maintaining the proposed model’s resistance to many malicious attacks on the network.

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations.

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors . We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

A Low-Cost IoT Based Buildings Management System (BMS) Using Arduino Mega 2560 And Raspberry Pi 4 For Smart Monitoring and Automation

This work presents an internet of things (IoT) based building management system (BMS) for monitoring, control, and energy management in buildings to provide an efficient way of energy utilization. Existing systems mainly provide monitoring of different parameters with limited controlling/automation functions. Existing solutions also do not provide automatic decision-making, advanced safety management, and resource tracking. However, the proposed system provides a comprehensive way of monitoring, controlling, and automatic decision making regarding different environmental and electrical parameters in buildings, i.e., temperature, humidity, dust, volt, etc., by using a low-cost wireless sensor network (WSN). The architecture of the proposed system consists of five layers and uses analog sensors which are connected to Arduino Mega 2560 microcontrollers for data collecting, NodeMCUs ESP8266 for wireless communication, Raspberry Pi4 microcomputers for decision making, and nod-RED dashboard which runs locally on a Raspberry Pi 4to provide a friendly end-user interface. The system also uses the Message Queuing Telemetry Transport (MQTT) communication protocol through Wi-Fi and completely relies on the local devices in the architecture and does not need cloud computing services. The proposed system provides two different kinds of automation, i.e., safety automation for the safety of different devices with advanced features, and energy automation. The proposed system is also able to provide humidity control inside a room and to track and count the available resources in any facility. The proposed system is low cost, scalable, and can be used in any building. Simulation results show that the proposed system is highly efficient.

Wireless Sensor Network Dependable Monitoring for Urban Air Quality

This paper presents an Internet of Things-enabled low-cost wireless sensor network with newly-developed dependable schemes to improve reliability for monitoring air quality in suburban areas. The system features sensing units for router communications with energy savings from dynamic conservation. Based on the reliability function and mean time to failure, a continuous time Markov chain model is used to analyze the monitoring performance. The proposed dependable monitoring network is shown to achieve high availability with regards to energy consumption and data assurance with the survival probability of over 80% during a minimum period of 72-hour operation for monitoring air quality in a suburb. Distributions of fine particle concentrations studied over a 6-month period demonstrate feasibility of the developed system in its high correlations to benchmark monitoring stations with the Pearson’s coefficients obtained at 0.903 and 0.817 respectively for PM2.5 and PM10. Statistical analysis is conducted for performance evaluation in association with two extreme events, one with bushfires and the other with pandemic lockdown. The results obtained indicate enhancements in reliability and accuracy of the colocated dependable low-cost sensors network proposed for wireless monitoring of air quality in urban conditions.

WSN Localization Using Similarity-Based Ricci Flow Embedding

In low-cost Wireless Sensor Networks (WSN), node localization in a noisy environment is a significant problem as additional hardware like GPS is avoided due to high cost. The pairwise distances between sensor nodes are estimated using the Received Signal Strength Indicator (RSSI) because of the low computational complexity and cost-effectiveness. However, the RSSI distance estimates are rough and fluctuate owing to various environmental conditions like noise, interference, and fading. This results in non-Euclidean pairwise distance estimates. To compute the actual inter-node distances, we consider the manifold assumption that the nodes embedded in the low dimensional space apparently lie in an unknown high dimensional manifold. The inter-node distances can be estimated by reducing the dimension through manifold learning. This work proposes a localization technique based on Similarity-Based Ricci Flow Embedding (SBRFE), an unsupervised manifold learning method that reduces the non-Euclidean artefacts of a distance matrix and converts it into a Euclidean one. Each inter-node distance is considered independently, and the corresponding sectional curvature is updated iteratively till all the distances become Euclidean. Experiment results show that the SBRFE algorithm outperforms all the conventional manifold learning algorithms and can localize all the sensor nodes with an increased accuracy up to 94.55%.