Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

Henna, Shagufta

doi:10.1155/2017/7090782

Cited by 21 publications

(12 citation statements)

References 52 publications

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“…When the clustering node obtains the binary data's from the source node, it will recognize the node failures of the source location based on the received data by categorizing the faulty data and using SVMSFDLA technique minimizes the packet drop, delay, transmission overhead and increases reliability. From the existing papers [21][22][23][24][25][26][27][28][29][30], the authors have concentrated only on to detect the faults rather than the corresponding loss recovery system. SFDLA technique [31] overcomes that problem by the combined contributions, but the employed SVM-based failure detection consumed much more power.…”

Section: Problem Methodology and Network Model 31 Problem Methodologymentioning

confidence: 99%

“…Henna et al [28] have proposed an ideal solution to the excess energy spent during the relay action of the fault tolerance processes of wireless sensor networks. They have come up with approximation which could be proved mathematically and they have called it EMDC: Energy Efficient Maximum Disjoint Coverage.…”

Section: Related Workmentioning

confidence: 99%

“…In the second scenario, we vary the number of faults by 5, 10, 15, 20, and 25 with fixed number of nodes as 120. The total simulation will take the time period of 1000 s. The performance of proposed Q-ODA technique is analysed with the prevailing techniques and specifically DFTR [21], PDAFT [22], FIDA [24], EMDC [28], MDFU [30] and SFDLA [31]. The comparisons are done in Algorithm 2 Fault detection using multi-criteria moth-flame decision-making (MMFD).…”

Section: Simulation Parameter and Setupmentioning

confidence: 99%

“…SH acts as H in the event of header node failure 4. The proposed Q-ODA is computed using network simulator 2 tool and the results are compared with the existing methodologies namely DFTR [21], PDAFT [22], FIDA [24], EMDC [28], MDFU [30] and SFDLA [31] and the simulation results emphasis our proposed work an edge over than the above methodologies specifically in relations with energy consumption, End-to-End delay, data delivery ratio, data accuracy, network lifetime, failure rate, and cost of data aggregation.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Data Aggregation Scheme for Wireless Sensor Network Using Qos Parameters With Efficient Failure Detection and Loss Recovery Technique

Basha¹,

Yaashuwanth²

2019

NNW

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WSN: Wireless Sensor Networks play a significant part in its modern era but its limited power supply acts as a blocking stone in it growth. In order to save energy in WSN the concept of aggregator node is introduced, where the aggregator node would act as a mid-point between the source and destination node during the data transmission. The data aggregation process creates major problems like excess energy expenditure, and delay. In the process of eliminating or reducing the delay and energy expenditure, the researchers have been handled in different ways. Applications like environment monitoring, target tracking, military surveillance and health care require reliable and accurate information. Many researchers have proposed data aggregation techniques to enhance the latency, average energy consumption and average network lifetime. However, these techniques are not sufficient to address situations like node failure and loss recovery. This paper proposes to build a solid wireless sensor system which concentrate on efficient optimal data aggregation along with additional QoS metrics such as failure detection and loss recovery. The first contribution of this paper is to propose an Improved Wolf Optimization (IWO) algorithm for clustering. The clustering process includes an efficient cluster formation like, Cluster Head (CH), and Sub Head (SH) selection. The second contribution of this paper is inclusion of failure detection and loss recovery. The former is developed based on Multi-criteria Moths-Flame Decisionmaking (MMFD) model and the latter is achieved through SH. SH node will act as the backup node for cluster head when failure instances are detection. CH recovers the lost data through SH, which minimize the additional delay of backup node selection process and save much more energy. The results are simulated using network simulator 2 tool and it is compared with existing techniques. The Network Simulator-2 results disclose that the findings are better than the available existing methodologies.

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Section: Problem Methodology and Network Model 31 Problem Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Simulation Parameter and Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Optimal Data Aggregation Scheme for Wireless Sensor Network Using Qos Parameters With Efficient Failure Detection and Loss Recovery Technique

Basha¹,

Yaashuwanth²

2019

NNW

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“…Henna et al, proposed proficient disjoint coverage algorithm achieve energy efficiency and fault tolerance by preparing different disjoint set covers with minimum relay energy. On one hand, spate disjoint set achieve fault tolerance by providing double coverage [20]. Farnaz Pakdel et al, [21] proposed the strategy which balances in the density of the cluster cause to reschedule the finish time of the cluster head node and minimizes the collision because of less energy balance in clusters.…”

Section: Introductionmentioning

confidence: 99%

Energy Attentive Pre-fault Detection Mechanism with Multilevel Transmission for Distributed Wireless Sensor Network

Talmale¹,

Mundukur²,

Thakare³

2019

RIA

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To recognize the vision of ambient intelligence, wireless sensor networks (WSNs) are broadly used to invent real time and smart applications. Energy has been a major concern in sensor networks. Another challenging issue of wireless sensor network is to discover the imperfect node and recover them to maintain network topology. Enhancement of network lifetime, reliability and scalability also depends on fault detection and recovery mechanism. Distributed Energy Attentive Pre Fault Detection Mechanism is proposed in this work for pre fault detection and multipath routing. The novel idea behind this approach is to recognize the sensor fault in advanced and performed the improvement action based on notification parameter like acknowledgement not received, residual energy is below threshold value and hope count distance. It enables a predesor node to rapidly identify and efficiently categories the next energy aware shortest path, upon steering failure, rather than relying from the starting place. Extensive simulations are performed and result demonstrates that the proposed algorithms perform well on various performance indicator metrics such as throughput, energy consumption, packet drop ratio, and communication delay.

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Fault‐tolerance based on augmenting approach in wireless sensor networks

Belkadi

Lehsaini

Tahraoui

2022

Concurrency and Computation

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A critical node is a sensor whose failure causes loss of connectivity and network fragmentation. In wireless sensor networks, the failure of a critical node, like the failure of all sensor nodes, can be caused by energy depletion or physical failure. To overcome the problem of failure of such nodes, this article proposes a fault-tolerant strategy that allows a routing protocol to tolerate the failure of a critical node. The proposed strategy is carried out in two phases. In the first phase, a hybrid genetic algorithm with a local search heuristic called genetic algorithm-critical node problem (GA-CNP) is used to select the critical nodes and in the second phase, an algorithm called Aug-CNP is involved to deal with the augmentation problem by deploying additional wireless edges to preserve network connectivity in case of critical node failure. Our proposal has been developed using the OMNET++ simulator, evaluated, and compared to the AODV protocol. The simulation results showed that the GA-CNP algorithm selects the critical nodes whose failure can degrade the network lifetime with a rate of 40%. Moreover, the Aug-CNP algorithm applied to the AODV protocol brings improvements in terms of network lifetime which reaches 22% compared to the traditional AODV protocol.

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Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

Cited by 21 publications

References 52 publications

An Optimal Data Aggregation Scheme for Wireless Sensor Network Using Qos Parameters With Efficient Failure Detection and Loss Recovery Technique

An Optimal Data Aggregation Scheme for Wireless Sensor Network Using Qos Parameters With Efficient Failure Detection and Loss Recovery Technique

Energy Attentive Pre-fault Detection Mechanism with Multilevel Transmission for Distributed Wireless Sensor Network

Fault‐tolerance based on augmenting approach in wireless sensor networks

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