scholarly journals A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

2015 ◽  
Vol 11 (5) ◽  
pp. 841462 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Node Localization

scholarly journals Monte Carlo Node Localization Based on Improved QUARTE Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ling Song ◽  
Xiaoyu Jiang ◽  
Liying Wang ◽  
Xiaochun Hu
Keyword(s):  
Monte Carlo ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Sampling Area ◽  
Node Localization ◽  
Positioning Accuracy ◽  
Anchor Nodes ◽  
The Impact

Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

scholarly journals An Efficient Localization Method for Mobile Nodes in Wireless Sensor Networks

2017 ◽  
Vol 13 (03) ◽  
pp. 160 ◽  
Author(s):  
Zhiyu Qiu ◽  
Lihong Wu ◽  
Peixin Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Node Localization ◽  
Location Algorithm

<p style="margin: 1em 0px; -ms-layout-grid-mode: char;"><span style="font-family: Times New Roman; font-size: medium;">With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.</span></p>

scholarly journals A NOVEL APPROACH OF CLUSTERING FOR ENHANCED LIFETIME IN WIRELESS SENSOR NETWORK

2017 ◽  
Vol 16 (7) ◽  
pp. 7031-7039
Author(s):  
Chamanpreet Kaur ◽  
Vikramjit Singh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Level ◽  
Research Work ◽  
Cluster Head ◽  
Mobile Node ◽  
Separation Distance ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor

Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The cluster head election mechanism will include various parameters like maximum residual energy of a node, minimum separation distance and minimum distance to the mobile node. Each CH will create a TDMA schedule for the member nodes to transmit the data. Nodes will have various level of power for signal amplification. The three levels of power are used for amplifying the signal. As the member node will send only its own data to the cluster head, the power level of the member node is set to low. The cluster head will send the data of the whole cluster to the mobile node, therefore the power level of the cluster head is set to medium. High power level is used for mobile node which will send the data of the complete sector to the base station. Using low energy level for intra cluster transmissions (within the cluster) with respect to cluster head to mobile node transmission leads in saving much amount of energy. Moreover, multi-power levels also reduce the packet drop ratio, collisions and/ or interference for other signals. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, multiple experiments have been conducted using different values of initial energy.

A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network

2020 ◽  
pp. 1522-1537 ◽  
Author(s):  
Essa Qasem Shahra ◽  
Tarek Rahil Sheltami ◽  
Elhadi M. Shakshuki
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Average Distance ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Military Operations ◽  
Localization Algorithms ◽  
Anchor Nodes ◽  
Routing Tables ◽  
Range Free

Wireless Sensor Network is deployed in many fields including military operations, mechanical applications, human services, smart homes, etc. However, deploying WSN encounters many challenges. One of the challenges is localizing the node position, especially mobile targets in critical situations. In this paper, the authors compare two types from range-free localization algorithms and one type from range-based algorithms, namely: Received Signal Strength (RSS), Centroid, and Distance Vector Hop (DV-Hops) protocols, using Cooja simulator. RSS localization algorithms require determining values of the RSS from the anchor nodes around the mobile node, to calculate the distance between the unknown mobile and the first three anchor nodes in the mobile range. The centroid localization requires only three anchors to compute the location of the mobile sensor without the need for distance measuring. Lastly, the DV-Hop algorithm uses routing tables of each anchor in the network topology to compute the Average Distance of Hops. The results show that rang-based algorithms are more accurate than range-free.

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