Humans are now in the information age, and sensor technology, as the most important and fundamental technology for information acquisition, has also seen significant development.
The development of sensor networks can be divided into three stages: sensors → wireless sensors → wireless sensor networks.
The first stage can be traced back to the traditional sensor systems used during the Vietnam War, which were actually systems composed of vibration and sound sensors.
The second stage, from the 1980s to the 1990s, primarily includes distributed sensor network systems developed by the U.S. military, naval collaborative combat capability systems, and remote battlefield sensor systems. These modern miniaturized sensors possess perception, computational, and communication capabilities.
The third stage, which started in the 21st century and continues to this day, is characterized by self-organizing network transmission and low-power node design, used in more scenarios beyond the military field.
Wireless Sensor Networks (WSN) are a multi-hop self-organizing network system composed of a large number of micro sensor nodes deployed in a monitored area, formed through wireless communication, with the purpose of collaboratively perceiving, collecting, and processing information about the sensed objects in the network coverage area and sending it to observers. Sensor, sensed objects, and observers constitute the three elements of a sensor network.
WSN is a distributed sensor network, where the endpoints are sensors that can perceive and inspect the external world. Sensors in WSN communicate wirelessly, making the network setup flexible, allowing device locations to be changed at any time, and also enabling wired or wireless connections to the internet.
Today, with the rapid development of the Internet of Things, wireless sensor network technology is an important underlying network technology for the widespread application of the Internet of Things, serving as the nerve endings of mobile communication networks and wired access networks, further extending network coverage.
Recently, the journal “Application of Electronic Technique” has published a series of articles related to Wireless Sensor Networks (WSN), including some excellent network design applications and positioning algorithms, which I have compiled here. Everyone is welcome to promote and cite!
1. An Improved Localization Algorithm Based on RSSI in WSN
Abstract: In the node localization algorithm for mobile wireless sensor networks, the RSSI-based MCL localization algorithm improves the prediction and filtering processes of localization using the log-normal model of received signal strength, enhancing localization performance, but still has drawbacks such as large computational load and high power consumption. Since the motion state of the object does not change abruptly, the trajectory of the previous moments can be used to predict the current motion parameters. Using Hermite interpolation, a good prediction of the current motion trajectory is made. Simulation results show that this algorithm reduces the sampling range and improves sampling accuracy compared to traditional algorithms, thereby enhancing localization accuracy and reducing power consumption.
Full Text Link: http://www.chinaaet.com/article/3000007384
Chinese Citation Format: Huang Haihui, Li Longlian. An improved localization algorithm based on RSSI in WSN [J]. Application of Electronic Technique, 2015, 41(1): 86-89.
English Citation Format: Huang Haihui, Li Longlian. An improved localization algorithm based on RSSI in WSN [J]. Application of Electronic Technique, 2015, 41(1): 86-89.
2. Intelligent Localization Algorithm in WSN Based on Multi-Power Mobile Anchor Nodes
Abstract: To reduce localization costs and improve localization accuracy, a SAPSO-SMPMA algorithm is proposed, which uses a single mobile anchor node to calculate the coordinates of unknown nodes. This algorithm employs a single mobile anchor node to traverse the localization area and transmits beacon signals with different power levels through power control. Unknown nodes utilize the information from anchor nodes at different positions combined with an adaptive weighted particle swarm algorithm to calculate their coordinates. Considering that anchor nodes may have errors in practical applications, vector error analysis of anchor nodes is included. Simulations show that this algorithm maintains high localization accuracy while significantly reducing localization costs by adequately considering the errors of anchor nodes, making it a practical localization algorithm.
Full Text Link: http://www.chinaaet.com/article/3000006623
Chinese Citation Format: Du Yangyang, Mao Yongyi. Intelligent algorithm for locating nodes in wireless sensor network based on the multi-power level mobile anchor node [J]. Application of Electronic Technique, 2015, 41(6): 88-90.
English Citation Format: Du Yangyang, Mao Yongyi. Intelligent algorithm for locating nodes in wireless sensor network based on the multi-power level mobile anchor node [J]. Application of Electronic Technique, 2015, 41(6): 88-90.
3. Fault Detection in WSN Based on Maximum Likelihood Estimation and Naive Bayes
Abstract: Faulty nodes in WSN lead to data transmission delays and increased energy consumption, and can also cause network congestion. To address this, a WSN fault node diagnosis and localization algorithm based on maximum likelihood estimation and Naive Bayes analysis is proposed. First, a large number of features are extracted from the protocol part of the data packets as a training dataset, from which marginal probabilities are estimated and a Naive Bayes classifier is built. Conditional probabilities are estimated using maximum likelihood estimation. In the detection phase, suspicious nodes are determined by checking if the transmission delay meets threshold conditions, and then the Naive Bayes classifier is used to detect faulty nodes, ultimately achieving successful classification of nodes.
Full Text Link: http://www.chinaaet.com/article/3000008373
Chinese Citation Format: Jing Mingmin, Xiao Li, Yang Chuanshu. Fault detection in WSN based on maximum likelihood estimation and Naive Bayes [J]. Application of Electronic Technique, 2015, 41(7): 114-117.
English Citation Format: Jing Mingmin, Xiao Li, Yang Chuanshu. Maximum likelihood estimation and Naive Bayes classifier based fault detection in WSN [J]. Application of Electronic Technique, 2015, 41(7): 114-117.
4. Implementation of Equipment Management System Based on Contiki and Active RFID
Abstract: The system implements an active RFID reader node based on Contiki, while also implementing an edge router. Together, the edge router and the reader node can form a wireless sensor network. Users can control the reader node through the network to collect and manage RFID tags, achieving equipment management. This system maintains the advantage of wide deployment range of wireless sensor networks, while integrating active RFID technology to reduce network complexity and lower system power consumption. The system operates stably and is suitable for managing equipment over a large area.
Full Text Link: http://www.chinaaet.com/article/3000017519
Chinese Citation Format: Dong Kun, Chen Bo, Zhao Zhongquan. Implementation of equipment management system based on Contiki OS and active RFID [J]. Application of Electronic Technique, 2016, 42(3): 57-60.
English Citation Format: Dong Kun, Chen Bo, Zhao Zhongquan. Implementation of equipment management system based on Contiki OS and active RFID [J]. Application of Electronic Technique, 2016, 42(3): 57-60.
5. Development of KW01-ZigBee Wireless Sensor Network Application Development Platform
Abstract: To solve the problems of high difficulty and long cycle in wireless sensor network application development, a four-layer architecture development platform model is proposed based on in-depth analysis of the causes and current status of wireless sensor network application development technology, following software engineering principles and basic theories of component design. Accordingly, a KW01-ZigBee-based wireless sensor network application development platform is developed, featuring clear architecture, rich external interfaces, and complete hardware and software components, detailing the entire development process from the design of development boards and hardware and software components to project framework design. Examples and operational tests using the development platform demonstrate that the developed platform is correct, practical, and user-friendly, capable of reducing technical difficulty in development and improving development efficiency.
Full Text Link: http://www.chinaaet.com/article/3000062325
Chinese Citation Format: Cai Bofeng, Cai Weida, Wang Yihuai. Design of KW01-ZigBee wireless sensor network application development platform [J]. Application of Electronic Technique, 2017, 43(3): 55-58.
English Citation Format: Cai Bofeng, Cai Weida, Wang Yihuai. Design of KW01-ZigBee wireless sensor network application development platform [J]. Application of Electronic Technique, 2017, 43(3): 55-58.
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