An effective graph‐theoretic approach towards simultaneous detection of fault(s) and cut(s) in wireless sensor networks

Swain, Rakesh Ranjan; Dash, Tirtharaj; Khilar, Pabitra Mohan

doi:10.1002/dac.3273

Cited by 24 publications

(18 citation statements)

References 20 publications

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“…12 Furthermore, if the number of nodes in the network is less, then isolating nodes for each kind of failure would result in a void structure in the whole network leaving the nodes to be incommunicable from other regions of the network. This computed information might further result in causing an either system failure or generating an unprecedented outcome that would affect the time and financial resources, and sometimes life.…”

Section: Fault Tolerance Phasementioning

confidence: 99%

“…value of the occurrence in the form of the Equation 12 and Equation 13 as t start (t + 1) = t 0 (t + 1) = 1 t 0 (t) + 2 Δ(t) + 3 .…”

Section: Figure 12mentioning

confidence: 99%

“…A few well-known critical applications of WSN such as patient health-care monitoring system, 2 military surveillance systems, 3 environment disaster monitoring systems, 4,5 industrial monitoring system, 6 and forest fire detection system 7 are highly dependent on the security 8 and accuracy 9 of the computation carried out in the sensor modules. 11,12 The frequent and unexpected occurrences of unusual behavior of sensor module that leads to unwanted characteristics of the sensor network are regarded as a "fault" in the sensor node. 11,12 The frequent and unexpected occurrences of unusual behavior of sensor module that leads to unwanted characteristics of the sensor network are regarded as a "fault" in the sensor node.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Swain

Khilar

Dash

2018

Int J Communication

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Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R 2 of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.

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Section: Fault Tolerance Phasementioning

confidence: 99%

“…value of the occurrence in the form of the Equation 12 and Equation 13 as t start (t + 1) = t 0 (t + 1) = 1 t 0 (t) + 2 Δ(t) + 3 .…”

Section: Figure 12mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Swain

Khilar

Dash

2018

Int J Communication

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“…A tradeoff between security level and energy consumption is also presented in this paper. A graph theoretic distributed protocol to detect faults and cuts in WSN is given by Swain et al…”

Section: Background Studymentioning

confidence: 99%

A cost‐effective attack matrix based key management scheme with dominance key set for wireless sensor network security

Ahlawat

2018

Int J Communication

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Summary To guarantee the proper functionality of wireless sensor network even in the presence of the potential threats, a well‐designed key management scheme is very important. The assumptions about attackers critically influence the performance of security mechanisms. This paper investigates the problem of node capture from adversarial view point in which the adversary intelligently exploits the different vulnerabilities of the network to establish a cost‐effective attack matrix. To counteract such attacks, the defender or the network designer constructs similar attack matrix. The defender will identify a set of critical nodes and use the key compromise relationship to assign a key dominance rank to each node of the network. The key dominance rank quantifies the possibility of attack on a particular node. It is used to determine the hash chain length. It is also used to improve the security of path key establishment as well as rekeying of the proposed scheme. The performance of the proposed scheme is analyzed with other existing schemes, and it is shown that it outperforms with increased resilience against node capture, reduced number of hash computations, reduced key compromise probability of proxy nodes, and reduced number of revoked links during rekeying process.

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“…Data sorting is a vital step in irritating to maintain one of the principal properties of an organization its information. 1 Data classification engages categorizing information to predefined levels or an object. For instance, to classified data based on its related content, file type, working platform, normal file size in megabytes or gigabytes, in to their appropriate groups or classes.…”

Section: Introductionmentioning

confidence: 99%

Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms

Abubakar

Khan

Nawi

et al. 2016

j comput theor nanosci

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Accelerated Particle Swarm Optimization (APSO) algorithm is one of the latest additions to the group of meta-heuristic nature inspired algorithms which provides derivative-free solutions to solve complex problems. Meanwhile, the Levenberg Marquardt Back propagation (LMBP) still it is not able to avoid local minimum. To deal with this problem, global search optimization technique has the ability to adjust the weight for NN (Neural Network) to avoid the local minima problem. This paper proposes an accelerated particle swarm optimization (APSO) is implemented in conjunction with Levenberg Marquardt back propagation (LMBP) algorithms to achieve faster convergence rate and to avoid local minima problem. The performances of the proposed Accelerated Particle Swarm Optimization Levenberg Marquardt (APSO_LM) algorithms compared by means of simulations on 7-Bit Parity and six UCI benchmark classification datasets. The simulation results show that the APSO-LM algorithm shows better performance than baseline algorithms in terms of convergence speed and Mean Squared Error (MSE).

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An effective graph‐theoretic approach towards simultaneous detection of fault(s) and cut(s) in wireless sensor networks

Cited by 24 publications

References 20 publications

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

A cost‐effective attack matrix based key management scheme with dominance key set for wireless sensor network security

Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms

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