scholarly journals Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network

2020 ◽  
Vol 20 ◽  
pp. 53-64
Author(s):  
Puneetpal Kaur ◽  
Mohit Marwaha ◽  
Baljinder Singh
Keyword(s):  
Sensor Node ◽  
Base Station ◽  
Position Estimation ◽  
Sensor Nodes ◽  
Node Localization ◽  
Underwater Acoustic ◽  
Aggregate Information ◽  
Underwater Acoustic Network ◽  
Node Location ◽  
Node Position

A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station.  Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network.  The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

Advancement in range based scheme for node localization of underwater acoustic networks

2018 ◽  
Vol 7 (2.27) ◽  
pp. 122
Author(s):  
Vinayak Khajuria ◽  
Manjot Kaur
Keyword(s):  
Deep Sea ◽  
Sensor Node ◽  
Position Estimation ◽  
Sensor Nodes ◽  
Node Localization ◽  
Underwater Acoustic ◽  
Reply Message ◽  
Acoustic Networks ◽  
Underwater Acoustic Network ◽  
Node Position

The underwater acoustic network is the type of network which is deployed under the deep sea or ocean to gather underwater information. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node localization has a high delay which reduces its efficiency. The technique needs to be designed which localize a number of nodes in less amount of time. This research is based on the advancement of the range-based scheme for node localization. In the proposed scheme mobile beacons are responsible for the node localization. The beacon nodes send beacon message in the network and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node. The sensor nodes which are already localized will not respond back to the beacon messages which reduce delay in the network for node localization. The simulation of proposed modal is performed in MATLAB and it shows that proposed scheme performs well in terms of a number of nodes localized.  

scholarly journals Node Localization using Naive Bayes Classifier and Trilateral Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 2467-2470
Keyword(s):  
Naive Bayes ◽  
Dimensional Space ◽  
Naïve Bayes ◽  
Base Station ◽  
Sensor Nodes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Node Localization ◽  
Node Location

Node localization is an important problem considered among the researchers in the area of Wireless Sensor Networks (WSN). The WSN is formed by a group of sensor nodes having limited energy and other resources that transfers data among each other or to a base station in an ad-hoc fashion. The estimation of the geo location (co-ordinates in the two-dimensional space) of the sensor nodes is essential for ensuring the QoS within the network. The different applications of WSN require varied level of accuracy in the estimation of the location of the sensor nodes. Different localization schemes are adopted in the literature for better estimation of the node location and each of them has both merits and demerits. This paper focuses on analyzing the different node localization mechanism used in the WSN and to identify various issues and challenges in the estimation of the node location. This paper also proposes an optimal approach with less computational effort and high accuracy in prediction based on trilateration algorithm and the RSSI (Received Signal Strength Indicator) values extracted from the target nodes antennas. The network is segmented in to different blocks of unequal size and the block number in which the node is present will be identified using the naive bayes classifier

scholarly journals Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

2021 ◽  
Author(s):  
Lismer Andres Caceres Najarro ◽  
Iickho Song ◽  
Kiseon Kim
Keyword(s):  
Sensor Node ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Healthcare Industry ◽  
Target Node ◽  
Node Localization ◽  
Localization Accuracy ◽  
Fundamental Limitations ◽  
Points Of View

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

2019 ◽  
pp. 1-14
Author(s):  
Alonshia S. Elayaraja
Keyword(s):  
Target Tracking ◽  
Sensor Node ◽  
Management Strategies ◽  
Extended Period ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensor Management ◽  
Node Localization ◽  
Time Optimal ◽  
Energy Consumption Rate

Many applications in wireless sensor networks perform localization of nodes over an extended period of time. Optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus, localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this chapter, a proposal plan to develop a Bayesian localized energy optimized sensor distribution scheme for efficient target tracking in wireless sensor network is designed. The sensor node localization is done with Bayesian average, which estimates the sensor node's energy optimality. Then the sensor nodes are localized and distributed based on the Bayesian energy estimate for efficient target tracking. The sensor node distributional strategy improves the accuracy of identifying the targets to be tracked quickly. The performance is evaluated with parameters such as accuracy of target tracking, energy consumption rate, localized node density, and time for target tracking.

A Node Authentication Model in Wireless Sensor Networks With Locked Cluster Generation

2021 ◽  
pp. 236-250
Author(s):  
C. R. Bharathi ◽  
Alapati Naresh ◽  
Arepalli Peda Gopi ◽  
Lakshman Narayana Vejendla
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Cluster Formation ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Generation ◽  
Secure Network

In wireless sensor networks (WSN), the majority of the inquiries are issued at the base station. WSN applications frequently require collaboration among countless sensor nodes in a network. One precedent is to persistently screen a region and report occasions. A sensor node in a WSN is initially allocated with an energy level, and based on the tasks of that sensor node, energy will be reduced. In this chapter, two proposed methods for secure network cluster formation and authentication are discussed. When a network is established then all the nodes in it must register with cluster head and then authentication is performed. The selection of cluster head is done using a novel selection algorithm and for authenticating the nodes. Also, a novel algorithm for authentication is used in this chapter. The validation and authorization of nodes are carried over by managing the keys in WSN. The results have been analyzed using NS2 simulator with an aid of list of relevant parameters.

scholarly journals Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammadjavad Abbasi ◽  
Muhammad Shafie Bin Abd Latiff ◽  
Hassan Chizari
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Sensor Node ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Network Coverage ◽  
Detection Model

Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.

scholarly journals Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Sana Messous ◽  
Hend Liouane
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Average Distance ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Multihop Wireless ◽  
Distance Vector ◽  
Node Position

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

An Accurate Localization Method for Video Sensor Network Nodes

2011 ◽  
Vol 225-226 ◽  
pp. 531-535
Author(s):  
Jun Xiang Gao ◽  
Yan Tian
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Minimum Mean Square Error ◽  
Sensor Nodes ◽  
Network Nodes ◽  
Practical Applications ◽  
Accurate Localization ◽  
Node Location ◽  
Video Sensor Networks ◽  
Video Sensor

Localization of the sensor nodes is a major obstacle for practical applications of video sensor networks. This paper present a novel localization technique based on vision in wireless sensor networks. On the assumption that sensor nodes can be recognized in an image, a sensor node firstly direct its Field-of-View (FoV) to an anchor or localized node, then we can get the orientation of anchor node relate to the sensor node to be localized. If two or more anchors can be found in sensing area, a series of equations will be obtained. They can be solved using minimum mean square error rule, and the solution of the overdetermined equations mentioned above is the estimation of the node position. The experiments indicate that a promising performance can be achieved in determining the exact node location using a small number of anchors.

scholarly journals A Novel Secret Location Sharing Scheme for Wireless Sensor Network

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
Monjul Saikia
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Node ◽  
Implementation Process ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Location Sharing ◽  
Sharing Scheme

The wireless sensor network is a collection of sensor nodes that operate collectively to gather sensitive data from a target area. In the process of data collection the location of sensor nodes from where data is originated matters for taking any decision at the base station. Location i.e. the coordinates of a sensor node need to be shared among other nodes in many circumstances such as in key distribution phase, during routing of packets and many more. Secrecy of the location of every sensor node is important in any such cases. Therefore, there must be a location sharing scheme that facilitates the sharing of location among sensor nodes securely. In this paper, we have proposed a novel secure and robust mechanism for location sharing scheme using 2-threshold secret sharing scheme. The implementation process of the proposed model is shown here along with results and analysis.

scholarly journals MOBILE NETWORKING FOR “SMART DUST” WITH RFID SENSOR NETWORKS

2013 ◽  
pp. 272-276
Author(s):  
SHYAM D. BAWANKAR ◽  
SONAL B. BHOPLE ◽  
VISHAL D. JAISWAL
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
Mobile Network ◽  
Base Station ◽  
Sensor Nodes ◽  
Node Localization ◽  
Fine Grained ◽  
Mobile Networking ◽  
Smart Dust ◽  
Large Scale Networks

Large-scale networks of wireless sensors are becoming an active topic of research.. We review the key elements of the emergent technology of “Smart Dust” and outline the research challenges they present to the mobile networking and systems community, which must provide coherent connectivity to large numbers of mobile network nodes co-located within a small volume. Smart Dust sensor networks – consisting of cubic millimeter scale sensor nodes capable of limited computation, sensing, and passive optical communication with a base station – are envisioned to fulfil complex large scale monitoring tasks in a wide variety of application areas. RFID technology can realize “smart-dust” applications for the sensor network community. RFID sensor networks (RSNs), which consist of RFID readers and RFID sensor nodes (WISPs), extend RFID to include sensing and bring the advantages of small, inexpensive and long-lived RFID tags to wireless sensor networks. In many potential Smart Dust applications such as object detection and tracking, fine-grained node localization plays a key role.

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