Path reestablishment in Wireless Sensor Networks

Kale, Preeti; Nene, Manisha J.

doi:10.1109/wispnet.2017.8300043

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Path reestablishment technique for DAT reconstruction is demonstrated in Kale and Nene. 35 The energy consumption model for DAT construction is presented in Kale and Nene 16 and heuristic techniques to construct NL improving DATs are devised. Scheduling DATs using local heuristics with ordering (SDLHO) algorithm for DAT reconstruction is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…An exhaustive survey in Yetgin et al 34 studies NL maximization and DA techniques, respectively. Path reestablishment technique for DAT reconstruction is demonstrated in Kale and Nene 35 . The energy consumption model for DAT construction is presented in Kale and Nene 16 and heuristic techniques to construct NL improving DATs are devised.…”

Section: Introductionmentioning

confidence: 99%

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Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Kale

Nene

2021

Int J Communication

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Summary In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state‐of‐art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN‐based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state‐of‐art. The values of ρ are observed for the existing RE‐based heuristics as well as the proposed NN‐based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade‐off associated with the overheads to reconstruct DATs while improving NL.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Kale

Nene

2021

Int J Communication

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“…Recent trends in technological growth have led to different advantages in modern day society; therefore, much scope exists for applied research on the energy efficiency of wireless sensor networks [7]. In WSNs, energy consumption is one of the major challenges as sensor nodes, and Cluster Heads have a short lifetime owing to demands in energy consumption [8,9]. In recent times, different research works have focused on providing an effective WSN system for energy consumption, and many algorithms have been proposed to minimize GPS for the sensor node location identification.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

2021

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Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

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Scheduling data aggregation trees to extend network lifetime in sensor networks

Kale

Nene

2020

Int J Communication

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SummaryIncreasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree‐based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV‐based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics‐based technique of Local Path Reestablishment (LPR) and greedy‐based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments, and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

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Path reestablishment in Wireless Sensor Networks

Cited by 9 publications

References 8 publications

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy‐Efficient Optimization in Wireless Sensor Networks

Scheduling data aggregation trees to extend network lifetime in sensor networks

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