A Node Localization Verification Model for WSN

Chen, Miao; Dai, Guoyong; Chen, Lina; Huang, Jin; Chen, Qingzhang

doi:10.1007/978-3-319-31854-7_27

Cited by 2 publications

(8 citation statements)

References 19 publications

(20 reference statements)

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“…Even though the secure localization protects the location estimation process from several attacks, the location estimation can still go wrong in some situations, e.g., compromised sensors report a false location intentionally. Therefore, many researchers have worked on the location verification in WSNs [10,11,12,13,14,15,16,17,18,19,20,21]. Location verification schemes can be categorized as in-region and on-spot verification [15].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, on-spot verification usually verifies whether the claimed location of the sensors is correct [11,12,13,14,15,16,17,18]. This type of verification mainly uses pre-knowledge of the sensor deployment, the hop-distance relationship, graph theory or neighborhoods’ observation to verify the claimed location of sensors.…”

Section: Related Workmentioning

confidence: 99%

“…There were more recent research works trying to eliminate the fixed anchors or to detect the unreliable anchors and sensors [ 16 , 17 , 18 ]. Perazzo et al suggested the location verification based on verifiable multilateration (VM) [ 11 ] in [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

“…For example, if the location of a sensor is suspicious, a sensor that is called the verifier collects the necessary information and performs location verification as shown in Figure 1b. This location verification problem can be formulated in many different mathematical ways by reflecting the characteristics of the algorithms [10,11,12,13,14,15,16,17,18,19,20,21]. …”

Section: Introductionmentioning

confidence: 99%

“…On-spot verification is to verify whether the true location of a sensor is the same as its claimed location, and in-region verification is to verify whether a sensor is within a certain region. In each category, several location verification schemes have been proposed to check the reported location of sensors with special requirements [10,11,12,13,14,15,16,17,18,19,20,21]. The special requirements, such as special hardware, the active participation of neighbors, a dedicated verifier and a trusted third party, impose a burden on the sensors in terms of hardware cost, communication overhead and computation overhead.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Kim

Song

2017

Sensors

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Abstract:In wireless sensor networks (WSNs), the accuracy of location information is vital to support many interesting applications. Unfortunately, sensors have difficulty in estimating their location when malicious sensors attack the location estimation process. Even though secure localization schemes have been proposed to protect location estimation process from attacks, they are not enough to eliminate the wrong location estimations in some situations. The location verification can be the solution to the situations or be the second-line defense. The problem of most of the location verifications is the explicit involvement of many sensors in the verification process and requirements, such as special hardware, a dedicated verifier and the trusted third party, which causes more communication and computation overhead. In this paper, we propose an efficient location verification scheme for static WSN called mutually-shared region-based location verification (MSRLV), which reduces those overheads by utilizing the implicit involvement of sensors and eliminating several requirements. In order to achieve this, we use the mutually-shared region between location claimant and verifier for the location verification. The analysis shows that MSRLV reduces communication overhead by 77% and computation overhead by 92% on average, when compared with the other location verification schemes, in a single sensor verification. In addition, simulation results for the verification of the whole network show that MSRLV can detect the malicious sensors by over 90% when sensors in the network have five or more neighbors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Kim

Song

2017

Sensors

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Multirobot Federating Method for Disjoint Segments in Wireless Sensor Network Based on Optimal Anchor Point and Local Priority

Jing

Chen

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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In order to solve the connectivity problem between multiple islands in the wireless sensor network (WSN) due to large-scale damage, a multirobot WSN island alliance method based on the local priority of the optimal anchor point is proposed. This method introduces the idea of priority restoration of local connectivity in weighted networks and uses the optimal anchor point election mechanism to construct connectivity paths. Compared with existing methods, LPRMA’s local preferential recovery thinking makes WSN island alliances more adaptable. And LPRMA’s trunk deployment strategy based on optimal anchor can significantly reduce the path length and the number of trunk deployments for connected recovery. Finally, through the comparison and simulation experiments with the existing methods, the results show that the proposed method can effectively improve the efficiency of WSN island alliance while satisfying the constraints of lower network delay and higher robustness.

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A Node Localization Verification Model for WSN

Cited by 2 publications

References 19 publications

An Efficient Location Verification Scheme for Static Wireless Sensor Networks

An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Multirobot Federating Method for Disjoint Segments in Wireless Sensor Network Based on Optimal Anchor Point and Local Priority

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