scholarly journals Robust and Cooperative Localization for Underwater Sensor Networks in the Existence of Malicious Anchors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4519 ◽  
Author(s):  
Wenyu Cai ◽  
Junlei Yang ◽  
Meiyan Zhang ◽  
Shiling Peng ◽  
Junyi Yang
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Localization Algorithm ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Robust Localization ◽  
Anchor Nodes

Precise and robust localization in three-dimensional underwater sensor networks is still an important research problem. This problem is particularly challenging if there are some malicious anchors among ordinary anchor nodes that will broadcast their locations falsely and deliberately. In this paper, we study how to self-localize large teams of underwater sensor nodes under the condition that some malicious anchor nodes mixed with ordinary anchors. Due to malicious characteristic of some deliberate anchor nodes, an iterative and cooperative 3D-localization algorithm for underwater sensor networks in the existence of malicious anchors is proposed in this paper. The proposed robust localization algorithm takes advantage of distributed reputation voting method within 1-Hop neighboring reference nodes to detect and eliminate malicious anchor nodes. Moreover, one kind of Minimum Mean Squared Error estimation based iterative localization method is applied to determine accurate location information. Additionally, we analyze and prove that our localization algorithm would have a bounded error when the number of malicious anchors is smaller than a certain threshold. Extensive simulation results are provided to demonstrate performance improvements comparing to traditional Minimum Mean Squared Error and Attack Resistant Minimum Mean Squared Error based localization methods in terms of localization accuracy and coverage ratio.

scholarly journals Improved DV-Hop Localization Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 9 (5) ◽  
pp. 1324-1330
Keyword(s):  
Mean Squared Error ◽  
Classical Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Minimum Number ◽  
Anchor Nodes ◽  
Error Method

Considering the emerging applications of Wireless Sensor Network, the localization of sensor nodes is very important in some applications. Hence in this paper, proposed an improved Distance-Vector hop technique to reduce the localization error generated in the classical method of range free DV-hop algorithm. The proposed algorithm targets to minimize the error introduced in the average hop-size value by the series of mathematical corrections like minimum mean squared error method, modifying the average hop-value value calculated between known sensor nodes with respect to error-term calculated by comparing the estimated distance with actual distance or Euclidean value of anchor nodes, by using dynamic coefficient of weight with respect to minimum number of hopes, calculating x and y co-ordinates of unknown by using 2-D-hyperbolic method. The final corrections on the x and y values are done by using some geometrical methods. Simulations and results are executed on three different evolution models of network by varying radius of communication range, anchor node percentage and number of sensor nodes

Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Meiyan Zhang ◽  
Wenyu Cai
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Autonomous Underwater Vehicles ◽  
Estimation Method ◽  
Location Estimation ◽  
Underwater Sensor Networks ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
3D Localization ◽  
Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

scholarly journals Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

2017 ◽  
Author(s):  
Junhai Luo ◽  
Liying Fan
Keyword(s):  
Sensor Networks ◽  
Time Synchronization ◽  
Sensor Nodes ◽  
Second Phase ◽  
Underwater Sensor Networks ◽  
Localization Algorithm ◽  
Two Phase ◽  
Phase Time ◽  
Anchor Nodes ◽  
Range Free

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

scholarly journals A Combined Localization Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaogang Qi ◽  
Xiaoke Liu ◽  
Lifang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Vehicle Tracking ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Environment And Health ◽  
Anchor Nodes ◽  
Positioning Algorithm

Wireless sensor networks (WSNs) are widely used in various fields to monitor and track various targets by gathering information, such as vehicle tracking and environment and health monitoring. The information gathered by the sensor nodes becomes meaningful only if it is known where it was collected from. Considering that multilateral algorithm and MDS algorithm can locate the position of each node, we proposed a localization algorithm combining the merits of these two approaches, which is called MA-MDS, to reduce the accumulation of errors in the process of multilateral positioning algorithm and improve the nodes’ positioning accuracy in WSNs. It works in more robust fashion for noise sparse networks, even with less number of anchor nodes. In the MDS positioning phase of this algorithm, the Prussian Analysis algorithm is used to obtain more accurate coordinate transformation. Through extensive simulations and the repeatable experiments under diverse representative networks, it can be confirmed that the proposed algorithm is more accurate and more efficient than the state-of-the-art algorithms.

scholarly journals Trilateration based localization method using mobile anchor in wireless sensor networks

2020 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
M.G. Kavitha ◽  
Vinoth Kumar Kalimuthu ◽  
T. Jayasankar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Future Research ◽  
Localization Algorithm ◽  
Localization Method ◽  
Coverage Ratio ◽  
Movement Trajectories ◽  
Mobile Anchor ◽  
Anchor Nodes

<p><span>Localization in wireless sensor networks (WSNs) is essential in many applications like target tracking, military applications and environmental monitoring. Anchors which are equipped with global positioning system (GPS) facility are useful for finding the location information of nodes. These anchor nodes may be static or dynamic in nature. In this paper, we propose mobile anchors assisted localization algorithm based on regular hexagons in two-dimensional WSNs. We draw a conclusion that the number of anchor nodes greatly affect the performance of localization in a WSN. An optimal number of anchor nodes significantly reduces the localization error of unknown nodes and also guarantees that unknown nodes can obtain high localization accuracy. Because of the mobility of anchor nodes high volume of sensing region is covered with less period of time and hence the coverage ratio of the proposed algorithm increases. Number of communications also decreases for the reason that the system contains log<sub>e</sub> (n) number of anchor nodes which leads to less energy consumption at nodes. Simulation results show that our LUMAT algorithm significantly outperforms the localization method containing single anchor node in the network. Movement trajectories of mobile anchors should be designed dynamically or partially according to the observable environment or deployment situations to make full use of real-time information during localization. This is the future research issue in the area of mobile anchor assisted localization algorithm.</span></p>

scholarly journals Heuristic Localization Algorithm with a Novel Error Control Mechanism for Wireless Sensor Networks with Few Anchor Nodes

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Yujia Sun ◽  
Xiaoming Wang ◽  
Jiyan Yu ◽  
Yu Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Error Control ◽  
Control Mechanism ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Geometric Feature ◽  
Localization Algorithm ◽  
Anchor Nodes ◽  
Error Control Mechanism

A novel iterative localization algorithm with high accuracy and low anchor node dependency for large-scale wireless sensor networks is proposed in this paper. At each iteration, blind nodes are located using a weighted linear least squares-based algorithm. To prevent errors in the blind nodes from propagating and accumulating throughout the network, an anchor geometric feature-based error control mechanism is used to select the nodes that participate in the localization and to estimate the localization confidence. The simulation results show that the algorithm can be used when only a few anchor nodes are involved. This algorithm is more advanced than traditional methods, which often require a large number of well-placed anchor nodes to operate appropriately. By optimizing the decision parameter v of the algorithm, the average localization error of the algorithm is approximately 0.43 meters. When the ratio of anchor nodes (the ratio of the number of anchor nodes to the number of sensor nodes in the network) is 1.25% (i.e., 5 anchor nodes for 400 sensor nodes), the received signal strength indicator (RSSI) variance is 8 dBm, and the radio range is 50 meters. A comparison of the proposed algorithm with global localization methods, including multidimensional scaling (MDS), semidefinite programming (SDP), and shortest-path access (SPA), shows that the proposed algorithm achieves higher location accuracy and stability when the number of anchor nodes is varied. The efficiency of the proposed localization algorithm is evaluated in a real sensor network, and the accuracy is high and robust to radio channel variance.

scholarly journals Localization of Sensor Nodes using Flooding in Wireless Sensor Networks

2013 ◽  
Vol 9 (3) ◽  
pp. 1153-1161
Author(s):  
Basavaraj K Madagouda ◽  
Varsha M Patil ◽  
Pradnya Godse
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Real Number ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Anchor Nodes

The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks (WSN). In mostly localization algorithms, one of the main methods to improve localization accuracy is to increase the number of anchor nodes. But the number of anchor nodes is always limited because of the hardware restrict, such as cost, energy consumption and so on. In this paper, we propose a novel which uses forwarding a query message in flooding technique for localization using anchor nodes and once a node localized it acts as virtual anchor node and it helps to localize remaining sensor nodes. It is scheme to increase and upgrade the virtual anchor nodes, while the real number of physical anchors is the same as before.

Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

2020 ◽  
Vol 10 (1) ◽  
pp. 94-109 ◽  
Author(s):  
Rekha Goyat ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Hye-Jin Kim ◽  
Se-Jung Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Research Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Anchor Nodes ◽  
Least Energy ◽  
Range Free

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

Security based on Received Signal Strength in Localization for Underwater Sensor Networks

2016 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Karamjeet Kaur ◽  
Gianetan Singh Sekhon
Keyword(s):  
Sensor Networks ◽  
Threshold Value ◽  
Critical Issue ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Research Subjects ◽  
Malicious Nodes ◽  
Intermediate Node ◽  
End To End Delay ◽  
End To End

Underwater sensor networks are envisioned to enable a broad category of underwater applications such as pollution tracking, offshore exploration, and oil spilling. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security critical issue as underwater sensor networks are typically deployed in harsh environments. Localization is one of the latest research subjects in UWSNs since many useful applying UWSNs, e.g., event detecting. Now day’s large number of localization methods arrived for UWSNs. However, few of them take place stability or security criteria. In purposed work taking up localization in underwater such that various wireless sensor nodes get localize to each other. RSS based localization technique used remove malicious nodes from the communication intermediate node list based on RSS threshold value. Purposed algorithm improves more throughput and less end to end delay without degrading energy dissipation at each node. The simulation is conducted in MATLAB and it suggests optimal result as comparison of end to end delay with and without malicious node.

scholarly journals Attack-resilient minimum mean-squared error estimation

2014 ◽  
Author(s):  
James Weimer ◽  
Nicola Bezzo ◽  
Miroslav Pajic ◽  
Oleg Sokolsky ◽  
Insup Lee
Keyword(s):  
Error Estimation ◽  
Mean Squared Error ◽  
Minimum Mean Squared Error ◽  
Squared Error
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