Research on WSN reliable ranging and positioning algorithm for forest environment

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

Applications of wireless sensor networks to improve occupational safety and health in underground mines

INTRODUCTION

The very complex and hazardous environment of underground mines may significantly contribute to occupational fatalities and injuries. Deploying wireless sensor network (WSN) technology has the potential to improve safety and health monitoring of miners and operators. However, the application of WSN in the industry is not fully understood and current research themes in this area are fragmented. Thus, there is a need for a comprehensive review that directly explores the contribution of WSNs to occupational safety and health (OSH) in underground mines.

METHOD

This study aims to conduct a systematic literature review on the existing applications of WSNs for improving OSH in the underground mining industry to pinpoint innovative research themes and their main achievements, reveal gaps and shortcomings in the literature, recommend avenues for future scholarly works, and propose potential safety interventions. The major contribution of this review is to provide researchers and practitioners with a holistic understanding of the integration of WSN applications into underground mine safety and health management.

RESULTS

The review results have been categorized and discussed under three predominant categories including location monitoring and tracking, physiological and body kinematics monitoring, and environmental monitoring. Finally, seven major directions for future research and practical interventions have been identified based on the existing research gaps including: (1) further applications of WSNs for underground mining OSH management; (2) application of WSNs from research to real-world practice; (3) big data analytics and management; (4) deploying multiple WSNs-based monitoring systems; (5) integration of WSNs with other communication systems; (6) adapting WSNs to the Internet of Things (IoT) infrastructure; and (7) autonomous WSNs.

Multiparameter optimization system with DCNN in precision agriculture for advanced irrigation planning and scheduling based on soil moisture estimation

Agriculture is a distinct sector of a country's economy. In recent years, new patterns have evolved in the agricultural industry. In conjunction with sensor scaling down and precision agriculture, the field of remote sensor networks, such as the wireless sensor network (WSN), was developed. Its major purpose is to make horticultural operations simpler to identify, assess, and manage. This paper uses the proposed DCNN to predict soil moisture and plan irrigation for precision agriculture farmers to reduce water consumption used for cultivation and increase production yield by comparing water content during various stages of plant growth and integrating IoT applications into agriculture. It also optimizes the water level for future irrigation decisions to maintain crop growth and water stability. The data must be served and stored in the form of a grid view, according to Apriori and GRU (gated recurrent unit). Using numerous sensor and parameter modelling methodologies, this system assists in the prediction of irrigation planning based on irrigation needs. The predicted parameters include soil moisture, temperature, and humidity. This observed experimental data supports smart irrigation in crop production with a high yield and little water use. DCNN has a 98.5% experimental result accuracy rate and the MSE value is predicted in DCNN 99.25% of the time.

The evaluation and optimization of calibration methods for low-cost particulate matter sensors: Inter-comparison between fixed and mobile methods

With the development of the air pollution control, the low-cost sensors are widely used in air quality monitoring, while the data quality of these sensors is always the most concern for users. In this study, data from nine air monitoring stations with standard PM instruments were used as reference and compared with the data of mobile and fixed PM sensors in Jinan, the capital city of Shandong Province, China. Data quality of PM sensors was checked by the cross-comparison among standard method, fixed and mobile sensors. And the impacts of relative humidity and size distribution (PM_2.5/PM₁₀) on the performance of PM sensors were evaluated as well. To optimize the calibration method for both fixed and mobile PM sensors, a two-step model was designed, in which the RH and PM_2.5/PM₁₀ ratio were both used as input parameters. We firstly calibrated the sensors with five independent models, and then all the calibrated data were linearly fitted by the LR-final model. In comparison with standard instruments, the LR-final model increased the R² values of the PM_2.5 and PM₁₀ measured by fixed sensors from 0.89 and 0.79 to 0.98 and 0.97, respectively. The R² values of PM_2.5 and PM₁₀ measured by the mobile sensors both increased to 0.99 from 0.79 and 0.62. Overall, the two-step calibration model appeared to be a promising approach to solve the poor performance of low-cost sensors.

Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks

Recently, wireless sensor network (WSN) has drawn wide attention. It can be viewed as a network with lots of sensors that are autonomously organized and cooperate with each other to collect, process, and transmit data around targets to some remote administrative center. As such, sensors may be deployed in harsh environments where it is impossible for battery replacement. Therefore, energy efficient routing is crucial for applications that introduce WSNs. In this paper, we present an energy efficient routing schema combined with clustering and sink mobility technology. We first divide the whole sensor field into sectors and each sector elects a Cluster Head (CH) by calculating its members' weight. Member nodes calculate energy consumption of different routing paths to choose the optimal scenario. Then CHs are connected into a chain using the greedy algorithm for intercluster communication. Simulation results prove the presented schema outperforms some similar work such as Cluster-Chain Mobile Agent Routing (CCMAR) and Energy-efficient Cluster-based Dynamic Routing Algorithm (ECDRA). Additionally, we explore the influence of different network parameters on the performance of the network and further enhance its performance.

Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks

In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles.

Energy Harvesting Chip and the Chip Based Power Supply Development for a Wireless Sensor Network

In this study, an energy harvesting chip was developed to scavenge energy from artificial light to charge a wireless sensor node. The chip core is a miniature transformer with a nano-ferrofluid magnetic core. The chip embedded transformer can convert harvested energy from its solar cell to variable voltage output for driving multiple loads. This chip system yields a simple, small, and more importantly, a battery-less power supply solution. The sensor node is equipped with multiple sensors that can be enabled by the energy harvesting power supply to collect information about the human body comfort degree. Compared with lab instruments, the nodes with temperature, humidity and photosensors driven by harvested energy had variation coefficient measurement precision of less than 6% deviation under low environmental light of 240 lux. The thermal comfort was affected by the air speed. A flow sensor equipped on the sensor node was used to detect airflow speed. Due to its high power consumption, this sensor node provided 15% less accuracy than the instruments, but it still can meet the requirement of analysis for predicted mean votes (PMV) measurement. The energy harvesting wireless sensor network (WSN) was deployed in a 24-hour convenience store to detect thermal comfort degree from the air conditioning control. During one year operation, the sensor network powered by the energy harvesting chip retained normal functions to collect the PMV index of the store. According to the one month statistics of communication status, the packet loss rate (PLR) is 2.3%, which is as good as the presented results of those WSNs powered by battery. Referring to the electric power records, almost 54% energy can be saved by the feedback control of an energy harvesting sensor network. These results illustrate that, scavenging energy not only creates a reliable power source for electronic devices, such as wireless sensor nodes, but can also be an energy source by building an energy efficient program.