Data Aggregation Trees with QoS in Sensor Networks

Kale, Preeti; Nene, Manisha J.

doi:10.1109/i2ct45611.2019.9033919

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…QoS is an essential requirement to assure guaranteed performance of the identified quality parameters [104]. Due to wide ranging nature of applications, the primary QoS parameters can be classified as: (i) coveragethe number of sensors required and their deployment, (ii) sensing mechanism, (iii) data accuracy, (iv) network life-time, (v) time criticality, and (vi) reliability.…”

Section: Requirements and Tradeoffs In Data Aggregation Approachesmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

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Section: Requirements and Tradeoffs In Data Aggregation Approachesmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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“…In Liu et al, 40 the network performance is improved using RE-based techniques. In previous studies, 25,39,41,42 the effect of DA ratio α on the quality of constructed DAT is investigated where α is the amount of data that can be combined into a packet. There is a cost associated with DAT scheduling.…”

Section: Related Workmentioning

confidence: 99%

“…In Lu et al, 38 technique to schedule nodes and their monitoring directions to improve NL are described. In Kale and Nene, 39 DAT construction techniques to improve NL using deterministic and probabilistic network model are discussed. In Liu et al, 40 the network performance is improved using RE‐based techniques.…”

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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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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Data Aggregation Trees with QoS in Sensor Networks

Cited by 6 publications

References 19 publications

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

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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