Reliability Analysis of Wireless Sensor Network for Smart Farming Applications

Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.

Section: Centrifugal Pump Operation Failure and Its Featuresmentioning

confidence: 99%

Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors

Chen

Wei

Wang

et al. 2022

“…Then it proposed a distributed location algorithm based on K nearest neighbor classification to further improve the positioning accuracy of a traditional K-Nearest Neighbor (KNN) algorithm, which determines the similarity to the node location information. Aiming at understanding the way that the redundancy and the node deployment affect the network reliability, reference [ 5 ] analyzed the design and implementation of a wireless sensor network for low-power and low-cost applications while calculating its reliability based on the real environmental conditions and arrangement of the nodes deployed in the field. Authors in [ 17 ] proposed an uncertain dynamic data stream clustering algorithm based on the interval numbers, which improved the clustering accuracy by 61%.…”

Section: Related Workmentioning

confidence: 99%

“…As universal methods, the Global Positioning System (GPS) and the BeiDou Navigation Satellite System (BDS) provide location services. However, their positioning accuracy is reduced significantly in buildings, indoors, or canyons [ 4 , 5 ]. That makes it difficult to obtain reliable positioning information.…”

Section: Introductionmentioning

confidence: 99%

“…The positioning accuracy of the mobile nodes depends on both the distance estimation of anchor nodes and the position calculation methods. Due to the complex environmental factors during wireless signal propagation such as environmental interference, reflection, refraction, multi-path, and none line of sight (NLOS) transmission [ 5 , 12 ], there are different degrees of error in distance estimation. At the same time, the inevitable defects of wireless sensor networks enlarge the input error, where the anchor node may have the problems of communication uncertainty [ 13 ], limited processing capacities, and insufficient power supply [ 14 ] of the sensor node.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Localization Method for Wireless Sensor Networks Based on Anchor Node Optimal Selection and Particle Filter

Luo

Liu

Yan

et al. 2022

In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system.

“…Initially used in the army, this kind of network is now found in several fields, varying from industrial surveillance to the measurement of environmental data, agriculture, home automation, fire detection, medical sector, etc. [1,[4][5][6][7][8][9][10]. These applications are responsible for monitoring a target point or area by recording a reaction and communicating it.…”

Section: Introductionmentioning

confidence: 99%

Area Coverage Maximization under Connectivity Constraint in Wireless Sensor Networks

Tossa

Ansari

Ezin

et al. 2022

Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors.