Abstract

Data aggregation is a widely adopted method to effectively reduce the data transmission volume and improve the lifetime of wireless sensor networks (WSNs). In the data aggregation networks, some parameters directly determine the delay of aggregation. In industrial applications, the data generated by different sensors have different requirements for delay or other QoS performance. In the previous study, a common strategy is that all kinds of data is aggregated into one frame when the condition is satisfied with a QoS requirement, which causes excessive energy consumption and severely impairs the lifetime of network. A Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and guarantee that the delay could be controlled within the corresponding QoS requirement constraint. The primary contributions of the DDAR scheme are the following: (a) The DDAR scheme makes data with different QoS requirement route to the sink along the different paths. The parameters of the aggregators in each path, such as aggregation deadline () and the aggregation threshold (), are configured according to the QoS requirements. Accordingly, energy consumption can be reduced without degrading the performance of data transmission. (b) Based on DDAR scheme, an improved DDAR scheme is proposed to further improve performance through fully utilize the residual energy in the nodes which are far from the sink. The frequency of aggregation of these nodes increases by reducing the value of and so as to further improve the energy efficiency and reduce delay. Simulation results demonstrate that compared with the previous scheme, this scheme reduces the delay by 25.01%, improves the lifetime by 55.45%, and increases energy efficiency by 83.99%. The improved DDAR scheme improves the energy efficiency by 33.97% and service guarantee rate by 10.11%.

Highlights

  • Industrial intelligent technology has attracted the considerable attention of the manufacturing industries of all countries in the world

  • Smart industrial wireless sensor networks (SIWSNs) is identified as an essential technology to pave the way to this goal [2,3,4]

  • Smart WSNs have been widely applied in industrial application scenarios, ranging from environment monitoring to urban health monitoring [14,16], vehicular communication networks [6,17], cyber-physical cloud systems [18,19,20,21,22], multi-channel cognitive radio networks [8,23], crowdsourcing networks [24,25,26], social network [27,28,29,30]

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Summary

Introduction

Industrial intelligent technology has attracted the considerable attention of the manufacturing industries of all countries in the world. QoS parameters setting (setting Nt and Tt to satisfy the QoS requirement of the most emergent data) can ensure that all the data meet their requirements of application, but the frequency of aggregation is improved and the lifetime is significantly reduced. Based on the premise of meeting different QoS requirements, the energy consumption and delay are reduced and the performances increase. A Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and ensure that all kinds of data meet their service requirements. The DDAR scheme is a novel data aggregation routing framework In this framework, each node configures only one set of parameters to satisfy a certain QoS requirement. DDAR scheme realizes the differentiated data aggregation routing in the true sense, and is able to significantly reduce energy consumption while ensuring that data transmission of data packets meets service requirement.

Related Work
Research on Data Aggregation Routing
Research on Delay Optimization
System Model
Probability
Packet
Problem Statements
Research Motivation
General Design of DDAR
10: End if
19: End while
2: Each aggregator replies a message to inform the tag of service to the
13: End if
Performance Analysis and Optimization
Optimization Performance on Service Guarantee Rate
Optimization Performances on Lifetime
13. Average
Optimization Performance on Energy Efficiency
16. Average
Findings
Conclusions
Full Text
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