Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture

Jawad, Haider Mahmood; Jawad, Aqeel Mahmood; Nordin, Rosdiadee; Gharghan, Sadik Kamel; Abdullah, Nor Fadzilah; Ismail, Mahamod; Abu-AlShaeer, Mahmood Jawad

doi:10.1109/jsen.2019.2940186

Cited by 96 publications

(58 citation statements)

References 30 publications

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“…In this study, a farmland wireless sensor network scenario is proposed [31][32][33] . It is assumed that N sensor nodes are randomly deployed in a two-dimensional rectangular region of X×Y, and the wireless sensor network is assumed to have the following properties:…”

Section: Network Modelmentioning

confidence: 99%

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Miao¹,

Zhao²,

Wu³

2021

International Journal of Agricultural and Biological Engineering

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Wireless sensor network (WSN) can achieve real-time data collection and transmission of environment, soil, meteorology, crop physiology and other information in agriculture. The data provided by WSN could be used for decision making and management, which is very important in precision agriculture. Wheat farmland wireless sensor network has the characteristics of wide coverage area, long planting period, inconvenient energy supply, and serious impact of crop environment on wireless signal transmission. Routing protocol is an important method to achieve long-term WSN monitoring by selecting an appropriate path with low energy consumption for data transmission. According to the phenomenon of uneven environment and channel parameters caused by intensive crop growth in farmland, a non-uniform clustering routing protocol based on effective energy consumption (UCEEC) was proposed in this work. The method combined with the characteristics of multi-path fading of farmland environment signals. The idea of image segmentation was introduced. Nodes with high similarity were divided into a cluster area by the dissimilarity between nodes in order to improve the intracluster communication performance. Meanwhile, a multi-hop path selection method between cluster-heads based on the estimation of two-hop effective energy consumption is designed. The energy consumption cost factor is calculated by the effective energy consumption and the average energy consumption within the cluster to achieve the minimum and balance of the overall energy consumption of the network. Simulation results show that, compared with the existing Maximum Residual Energy Based Routing (MREBR) protocol, minimum Energy Consumption Based Routing (MEC) routing protocols, UCEEC improves the energy balance effect between nodes, prolongs the network life cycle, and realizes efficient energy utilization of wireless sensor network data collection in the complex environment of wheat field.

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Section: Network Modelmentioning

confidence: 99%

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Miao¹,

Zhao²,

Wu³

2021

International Journal of Agricultural and Biological Engineering

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“…Gao et al, in their work, 29 have used a method of support vector machine (SVM) for the classification of the moisture data collected from the WSN. Jawad et al 30 have used particle swarm optimisation (PSO)‐based model for the analysis and classification of the data captured from the WSN network.…”

Section: Role Of Iot For Monitoring Soil Using Wsnmentioning

confidence: 99%

“…ANN‐based WSN networks 28 can have better localisation capability, which leads to better communication and power consumption in the network. PSO‐based WSN networks 30 are also used to have better connectivity and efficient power management along with analysis and classification of data. SVM‐based networks 29 have better fault‐finding capabilities.…”

Section: Role Of Iot For Monitoring Soil Using Wsnmentioning

confidence: 99%

Designing efficient soil resistivity measurement technique for agricultural wireless sensor network

Parashar

Mishra

2021

Int J Communication

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Summary This 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.

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“…Until recently, these domains faced many challenges, mainly due to the lack of wide-area connectivity, limited energy resources, and sometimes harsh environmental conditions that made the deployment of WSNs difficult or even impossible. Innovative and cutting-edge technological advances, however, have expanded the horizons with new low-cost enablers and energy-efficient wireless technologies [30] (like SigFox [58,59], LoRa [60][61][62], NB-IoT [61,63], GSM-IoT [64,65], and ZigBee [66][67][68]), that take us to places previously unexplored, allowing us to test, administrate and record the dynamics of such systems in secure and credible ways [69]. Some indicative works, that took place recently and have relevance to the current work, are listed below.…”

Section: Emerging Technologies For Smart Agriculturementioning

confidence: 99%

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

et al. 2020

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The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

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Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture

Cited by 96 publications

References 30 publications

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption

Designing efficient soil resistivity measurement technique for agricultural wireless sensor network

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

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