Wireless Sensor Networks In Agriculture Research Articles

Resource allocation design method based on WSN multi-dimension soil inspection

Abstract. With the industrialization of agriculture and the sharp rise of food prices, in order to promote the intensive development of natural resources and achieve economies of scale agriculture in production, people put forward the innovative strategy of "precision agriculture" to maintain the sustainable development of agriculture. To protect the environment and farmer communities and increase productivity. However, in the special context of precision agriculture, the problems related to energy consumption and electromagnetic wave propagation in complex environments have a significant impact on precision agriculture. In the case of no monitoring and harsh environment, wireless sensor networks (WSN) can complete the tasks of real-time monitoring and information collection. In the past, the understanding of WSN in agriculture was limited to unilateral detection, which led to the underutilization of the soil. This paper introduces an innovative solution for precision agriculture based on wireless sensor networks, which mainly focuses on the observation and impact assessment of soil water volume,PH value, temperature and pressure in agricultural production process. It aims to reduce the waste of natural resources and the use of fertilizer substances, while maintaining or improving the yield of farming, in order to better promote the development of WSN in the agricultural field.

An adaptive hexagonal deployment model for resilient wireless sensor networks in precision agriculture

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Enhancing agricultural wireless sensor network security through integrated machine learning approaches

AbstractWireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.

An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks

Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.

Integrating Machine Learning with Wireless Sensor Networks in Agriculture

This review paper explores the synergistic integration of Wireless Sensor Networks (WSNs) and Machine Learning (ML) in the realm of agriculture, presenting a comprehensive examination of how these technologies collectively contribute to precision farming and sustainable agricultural practices. The paper discusses the key components of this integration, including sensor technologies, communication protocols, data processing techniques, and the application of ML algorithms in optimizing decision-making processes for enhanced agricultural productivity.

Design of agricultural wireless sensor network node optimization method based on improved data fusion algorithm.

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.

Zigbee-based wireless sensor network in smart agriculture for crop production

Zigbee-based wireless sensor network in smart agriculture for crop production

Studies on energy efficient techniques for agricultural monitoring by wireless sensor networks

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments.

Retraction Note: Ultra-reliable low latency communication technique for agriculture wireless sensor networks

Retraction Note: Ultra-reliable low latency communication technique for agriculture wireless sensor networks

Research on node coverage in agricultural wireless sensor networks based on deterministic coverage

The application of wireless sensor network in agriculture has always been a concern. In the research of wireless sensor network extension, coverage problem is one of the hot issues in wireless sensor network. Research on the node coverage of agricultural wireless sensor networks based on deterministic deployment is of great significance to improve the coverage quality of agricultural wireless sensor networks and reduce energy consumption. According to the feature of deterministic coverage of wireless sensor network nodes in the agricultural field, this paper summarizes and analyzes the existing deterministic coverage schemes, and proposes to determine the minimum number of nodes according to the size of the repeated coverage area, which provides ideas for the problem of node coverage with deterministic area characteristics in the agricultural field.

Integration of Agricultural Wireless Sensor Networks to Web-of-Things Through an Edge-Computing-Enriched WSNs/WoT Gateway

To connect agricultural wireless sensor networks (WSNs) to web services and applications, the agricultural WSNs/WoT gateway is empowered with local data management and network maintenance functions for downstream WSNs in addition to traditional upstream data collection. This work demonstrates a low-cost, highly scalable, rapidly deployable web of things (WoT) gateway with edge computing capabilities. First, an agricultural WSNs/WoT topology is architected, connecting a ZigBee WSNs to the Web for remote monitoring the local environmental and agronomical information, and simultaneously for managing the solar-powered WSNs for a prolonged lifespan according to the instant data scrawled from the cloud. Second, a WSNs/WoT gateway is designed with the hardware platform Raspberry Pi 3, which serves multiple needs for bidirectional information exchange and local WSNs management. Finally, experimentation demonstrates the proposed hardware platform and architecture can perform edge computing, and efficiently realize the up and down transmission and distribution of data stream.

A Review of Wireless Sensor Networks in Agriculture

Abstract: Wireless sensor network (WSN) contains various sensor nodes with the capacity of sensing, computing, and wireless communications. WSN technology is used to control and monitor the environmental and soil parameter in agricultural field. WSN employ as a part of agriculture for few reasons like gives high interpretation, increase the production of the crop, lowpower consumption and gather distributed data. Efficient management of water assumes an important part in agriculture. Various agricultural parameters like soil moisture, atmospheric temperature and humidity etc. are monitored and controlled by Monitoring and Controlling Units. WSN have fascinated much attention in various research areas like health care monitoring, environment monitoring and structural health monitoring. Recently WSN is widely used to provide solution on precision agriculture to overcome various problems in the real-world (field).This paper reviews the monitoring of precision agriculture using wireless sensor network focuses on literature of the development of a wireless sensor network on agricultural environment to monitor environmental conditions and deduce the appropriate environmental and agriculture. Agriculture and farming is one of the industries which has recently started using WSN to increase the productivity and standardize the agricultural yield at affordable cost.

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture.

Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function

Wireless sensor networks (WSN) enable the acquisition of multisource environmental data and crop states in precision agriculture. However, the complex agricultural environment causes the WSN topology to change frequently and link connection probability is difficult to predict. In order to improve the utilization of network resources and balance the network energy consumption, this paper studies an agricultural fault-tolerant topology construction method based on the potential game and cut vertex detection. Considering the connectivity redundancy, node lifetime, and residual energy, a fault-tolerant topology algorithm for agricultural WSN based on a double price function is designed. The network is clustered according to the node location and residual energy to form a single-hop effective cluster. Based on the network cluster, the price function is constructed in order to reduce energy consumption and balance network energy efficiency. The initial transmit power set supporting inter-cluster communication is obtained by potential game theory. While preserving the game characteristics of topology, the redundant links are eliminated and the transmit power is adjusted by a cut vertex detection algorithm to realize the construction of a 2-connected cluster head network. Simulation results show that the network topology constructed by the studied algorithm can balance the energy consumption and prolong the network lifetime effectively.

RETRACTED ARTICLE: Research on data transmission model of agricultural wireless sensor network based on game theory

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis

This study investigates how wireless sensor network (WSN) applications in agriculture are discussed in the current academic literature. On the basis of bibliometric techniques, 2444 publications were extracted from the Scopus database and analyzed to identify the temporal distribution of WSN research, the most productive journals, the most cited authors, the most influential studies, and the most relevant keywords. The computer program VOSviewer was used to generate the keyword co-occurrence network and partition the pertinent literature. Findings show the remarkable growth of WSN research in recent years. The most relevant journals, cited countries, and influential studies were also identified. The main results from the keyword co-occurrence clustering and the detailed analysis illustrate that WSN is a key enabler for precision agriculture. WSN research also focuses on the role of other technologies such as the Internet of Things, cloud computing, artificial intelligence, and unmanned aerial vehicles in supporting several agriculture activities, including smart irrigation and soil management. This study illuminates researchers’ and practitioners’ views of what has been researched and identifies possible opportunities for future studies. To the authors’ best knowledge, this bibliometric study represents the first attempt to map global WSN research using a comprehensive sample of documents published over nearly three decades.

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.

Design implementation of wireless multimedia sensor networks for dryland agriculture

This study discusses the application of Wireless Sensor Network (WSN) technology to specific dry land agriculture because it was founded in the Province of East Nusa Tenggara (NTT) and a small part of Maluku. Its uniqueness has become a comparative advantage for the regions, especially for the academics of the University of Nusa Cendana (Undana). In general, the use of WSN in agriculture is still basic, relying on analog and manual sensors to obtain information on land conditions and vegetation in the land. Also generally still using scalar data and not optimizing the use of multimedia data, especially data based on videos and images. The contribution and novelty of this research are: (a) The application of multi-sensor technology with the Wireless Multimedia Sensor Network (WMSN) will optimize various data from land conditions so that it is hoped that more analysis of monitoring data results is expected, precise and efficient; (b) The application of WMSN with Internet-of-Thing (IoT) will be applied to the dry land agriculture. This research produces a design for implementing WMSN technology on dry agricultural land which is expected to increase agricultural yields in the area.

RETRACTED ARTICLE: Ultra-reliable low latency communication technique for agriculture wireless sensor networks

RETRACTED ARTICLE: Ultra-reliable low latency communication technique for agriculture wireless sensor networks