Survey on Key Pre Distribution for Security in Wireless Sensor Networks

Rani, T. P.; Kumar, C. Jaya

doi:10.1007/978-3-642-27299-8_26

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Step 1: For each node i, the KDC randomly chooses a vector s i with a weight of 3 √ w/2 and publishes it.…”

Section: ) Generation Of the Public Key And Private Key For Space Nodesmentioning

confidence: 99%

“…Step 2: Node i generates its circulant sub-matrix S i by s i and chooses vector h i,2 and h i, 3 . Here, wt(h i,2 ) = 3 √ w/2, wt(h i,3 ) = w/2.…”

Section: ) Generation Of the Public Key And Private Key For Space Nodesmentioning

confidence: 99%

“…MD5 is used to map the identity to h ID i . H i,1 is generated by using formula (3). The other parameters such as s i , h i,2 , h i,3 used in the proposed scheme are generated randomly.…”

Section: F Error Correction Capacitymentioning

confidence: 99%

“…The key management schemes can be summarized into three categories by their characteristic [3]: centralized key management, distributed key management and decentralized…”

Section: Introductionmentioning

confidence: 99%

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A Centralized Key Management Scheme Based on McEliece PKC for Space Network

et al. 2020

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Constrained by the limited resource, high-latency and high bit error rate, the existing group key management schemes for the space network are inefficient. To solve this problem, we proposed a centralized and identity-based key management scheme by using McEliece public-key cryptosystem (PKC). In this scheme, the node identity is used as the parameter to generate the public key. Therefore, the authentication can be embedded into the verification of the public key without needing the PKI. The group key is distributed with the protection of the public key so that the group key management scheme can be implemented safely. Furthermore, the McEliece public-key cryptosystem can resist the quantum attack and provide error correction capacity. It improves the efficiency of the group key distribution over the noisy channel. The proposed key management scheme is simulated on OPNET. The security of public-key generation, forward secrecy, backward secrecy and performance are analyzed. The results show that our scheme can provide confidentiality, integrity, authentication, non-repudiation, failure tolerance and error correction. In addition, the computation overhead and rounds of interaction are lower than former work.

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“…Step 1: For each node i, the KDC randomly chooses a vector s i with a weight of 3 √ w/2 and publishes it.…”

Section: ) Generation Of the Public Key And Private Key For Space Nodesmentioning

confidence: 99%

“…Step 2: Node i generates its circulant sub-matrix S i by s i and chooses vector h i,2 and h i, 3 . Here, wt(h i,2 ) = 3 √ w/2, wt(h i,3 ) = w/2.…”

Section: ) Generation Of the Public Key And Private Key For Space Nodesmentioning

confidence: 99%

“…MD5 is used to map the identity to h ID i . H i,1 is generated by using formula (3). The other parameters such as s i , h i,2 , h i,3 used in the proposed scheme are generated randomly.…”

Section: F Error Correction Capacitymentioning

confidence: 99%

“…The key management schemes can be summarized into three categories by their characteristic [3]: centralized key management, distributed key management and decentralized…”

Section: Introductionmentioning

confidence: 99%

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A Centralized Key Management Scheme Based on McEliece PKC for Space Network

et al. 2020

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“…The wireless sensor networks (WSNs) are applied in many applications ranging from military to health care . The sensor nodes are bounded by memory space, energy, and computational power . These nodes work in an infrastructureless and dynamically changing environment and direct the amassed data to the sink node for further interpretation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

Vijayakumar

Ganeshkumar

Anandaraj

et al. 2018

Int J Communication

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Summary In this paper, a fuzzy logic–based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%.

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