Wireless Sensor Networks In Agriculture Research Articles

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.

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

REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

Designing efficient soil resistivity measurement technique for agricultural wireless sensor network

SummaryThis paper presents the study and method for measuring the electrical properties of the agricultural soil using a wireless sensor network to counter the dynamic changes in electrical properties of soils under variable rainfall and flood conditions. Soil moisture and nutrients can be correlated with the resistance of soil, which is the essential electrical property of the soil. Resistivity can help us to classify different agricultural soil types and can be used for real‐time monitoring of crop fields. The diverse resistivity soil patterns may help to classify the physical, chemical and biological properties of soil. In our research work, we have taken soil from different districts of Madhya Pradesh state and classified it based on resistivity. We have also designed a low‐cost resistivity sensor module with a mathematical justification of the results. The new sensor module has been tested on different soil samples of Madhya Pradesh, and wireless sensor network (WSN) has been proposed to collect and process the data for application in the agriculture fields. The paper also describes the challenges and the various methodologies for the measurement of soil resistivity and moisture using the Internet of Things (IoT) architecture.

An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network

AbstractAgriculture is a fundamental pillar of society, bearing a crucial role in shaping the economy of the country and provides huge financial prospects and the possibility of employment to the large mass of the country. Thus, the incorporation of modern technology would lead to amplification in productivity and economic escalation. In this perspective, Wireless Sensor Network (WSN) can provide a greater service by sensing, monitoring and supplying continuous information about climatic conditions like temperature, light, soil moisture, humidity, and so on. These parameters help in increment of productivity in terms of quantity and quality growth in agriculture. However, WSN brings security threats like masquerading, alteration, interference, interception, and so on, that harmfully affect crop production and other agricultural practices. Thus, privacy preservation and security enhancement for agricultural WSN are the major challenges to be conquered. To meet this purpose, a privacy‐preserving efficient and secure remote user authentication scheme is introduced in this research work for the application of agricultural WSN. The proposed scheme is formally evaluated using probabilistic Random‐Oracle‐Model (ROR) for proving the robustness of the scheme. Further, the scheme is simulated using a well‐accepted AVISPA tool to show the security strength of it. The performance analysis of our scheme shows that along with its robustness in terms of security, the proposed scheme is more efficient in terms of computation and communication overheads in contrast to other existing schemes. These aforesaid features make the proposed scheme implementable in a practical environment.

Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

In a wireless sensor network, the sensor nodes transmit the acquired information to the server through the data transmission link. On the serverside, the data are processed, fused, and expressed to serve the user. Sensor deployment is a key factor related to the stability and security of wireless networks. This article uses environmental changes to drive related technologies to deploy wireless sensors. In this article, environmental change-driven means that through certain deployment cost model assumptions and problem descriptions, network deployment is artificially divided into two stages: initial deployment and redeployment. In the deployment phase, by referring to the idea of virtual force, a new sensor deployment algorithm is proposed in the redeployment phase, which can well solve the stability- and security-related issues encountered in agricultural wireless sensor networks. In this algorithm, the moving distance of the mobile receiver and the average coverage of the network are calculated based on the virtual force, the direction, and the number of adjacent clusters. Finally, the algorithm model was simulated in MATLAB, and the feasibility of the algorithm was verified by analyzing the event coverage and the moving distance of nodes. The final simulation results show that the algorithm proposed in this paper can achieve better performance than existing algorithms in terms of average coverage and moving distance.