Secure localization using hypothesis testing in wireless networks

Al-Roomi, Suood Abdulaziz; Ahmad, Imtiaz; Dimitriou, Tassos

doi:10.1016/j.adhoc.2018.03.008

Cited by 7 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these types of applications, sensor nodes are distributed in a random scattering style through aerial vehicles or some other way at the desired location. However, in these types of applications, accurate knowledge of node location is extremely important to detect the source of sensed information and to take required actions based on this obtained sensed information [9]. 2023 Furthermore, it is highly essential to have knowledge of the source of data generation to analyze sensed data.…”

Section: Introductionmentioning

confidence: 99%

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

Nandyala¹,

Gandhi²

2023

IJ-AI

View full text Add to dashboard Cite

<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

Nandyala¹,

Gandhi²

2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…In such cases, the locations of nodes will be unknown. But, location information of nodes is essential for these applications to identify the origins of sensed data and to act on them [8]. Attaching Global Positioning System (GPS) receivers with every node is not a possible option because of increased cost, power consumption, and lower accuracy due to shadowing effects.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Bhat

Santhosh

2021

IEEE Access

View full text Add to dashboard Cite

Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

show abstract

Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

Gebremariam,

Panda,

Indu

2023

Alexandria Engineering Journal

View full text Add to dashboard Cite

Secure localization using hypothesis testing in wireless networks

Cited by 7 publications

References 15 publications

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

Contact Info

Product

Resources

About