Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

Kale, Preeti; Nene, Manisha J.

doi:10.1016/j.comnet.2019.05.017

Cited by 14 publications

(31 citation statements)

References 30 publications

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“…Some work in the literature also considers in-network processing. In [19], data aggregation trees are constructed and scheduled, and the network can be reconfigured, in that different trees can be used in different time periods. This work again uses the traditional WSN model of many homogeneous sensor nodes all sending measurements to a single sink.…”

Section: Related Workmentioning

confidence: 99%

Network lifetime maximization in wireless mesh networks for machine-to-machine communication

2019

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In this paper we present new optimization formulations for maximizing the network lifetime in wireless mesh networks performing data aggregation and dissemination for machine-tomachine communication in the Internet of Things. We focus on heterogeneous networks in which multiple applications co-exist and nodes may take on different roles for different applications. Moreover, we address network reconfiguration as a means to increase the network lifetime, in keeping with the current trend towards software defined networks and network function virtualization. To test our optimization formulations, we conducted a numerical study using randomly-generated mesh networks from 10 to 30 nodes, and showed that the network lifetime can be increased using network reconfiguration by up to 75% over a single, minimal-energy configuration. Further, our solutions are feasible to implement in practical scenarios: only few configurations are needed, thus requiring little storage for a standalone network, and the synchronization and signalling needed to switch configurations is low relative to each configuration's operating time.

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Section: Related Workmentioning

confidence: 99%

Network lifetime maximization in wireless mesh networks for machine-to-machine communication

2019

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“…Section 2). The ρ metric is evaluated for the state‐of‐art DAT scheduling heuristics 15,16 A new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN) is proposed (Ref.…”

Section: Introductionmentioning

confidence: 99%

“…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 16 .…”

Section: Introductionmentioning

confidence: 99%

“…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 16 . SDLHO employs an energy‐aware heuristics in which the decision of DAT reconstruction depends on the RE of the nodes.…”

Section: Introductionmentioning

confidence: 99%

“…SDLHO employs an energy‐aware heuristics in which the decision of DAT reconstruction depends on the RE of the nodes. This work estimates the energy consumption cost for DAT construction using the energy consumption model employed in Kale and Nene 16 . In Nguyen et al, 15 the researchers present Local Tree Reconstruction Based Scheduling Algorithm (LTRBSA) for DAT scheduling using energy‐aware heuristics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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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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Scheduling of data aggregation trees using Local Heuristics to enhance network lifetime in sensor networks

Cited by 14 publications

References 30 publications

Network lifetime maximization in wireless mesh networks for machine-to-machine communication

Network lifetime maximization in wireless mesh networks for machine-to-machine communication

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

Scheduling data aggregation trees to extend network lifetime in sensor networks

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