Network-Lifetime Maximization of Wireless Sensor Networks

Yetgin, Halil; Cheung, Kent Tsz Kan; El‐Hajjar, Mohammed; Hanzo, Lajos

doi:10.1109/access.2015.2493779

Cited by 54 publications

(33 citation statements)

References 52 publications

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“…Nearly similar form of the work has been carried out by Park et al [8]. Adoption of cross layer optimization using genetic algorithm towards solving scheduling, allocation of power, and data communication has been seen in the work of Yetgin et al [9]. Modelling of cross layer for incorporating interaction among Physical, MAC, Network, and Application layer for enhancing the energy efficiency was developed by Jemeli et al [10].…”

Section: Introductionmentioning

confidence: 84%

CLIF: Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN

Chandra¹,

Patil²,

Talanki³

2017

IJIES

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Adoption of Cross-Layer based techniques in Wireless Sensor Network (WSN) has been reported to be an efficient solution towards improving energy efficiency, scheduling related problems among the resource-limited sensor nodes. The existing approaches are reviewed to ensure that there exists further scope for the improvement and enhancement in terms of resolving the issues associated with even faster interaction among the layers of the conventional protocol stack for catering up and facilitating the demands of faster response time in real-time applications. Therefore, Cross Layer Interactive Framework (CLIF) a novel and advanced approach is proposed for essentially sustaining symmetry between the crucial performance parameters like data delivery and energy efficiency for a given dynamic communication circumstances in WSN. In addition, to this message prioritization is incorporated in CLIF for processing those messages with high degree of significance. Predominantly CLIF can be used for the diverse time critical and mission critical IoT applications. The results demonstrate that the proposed CLIF scheme offers better energy efficiency and enhanced data delivery services in WSN when compared with the existing cross-layer approaches.

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Section: Introductionmentioning

confidence: 84%

CLIF: Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN

Chandra¹,

Patil²,

Talanki³

2017

IJIES

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“…The NL in [8] was also defined as the instant, when the first data collection failure occurred. Note that there are various alternative NL definitions, which were discussed in [3], [8], [10], [13], [31], [34]- [36]. In Table I, we provide a comprehensive list of the NL definitions considered in the literature.…”

Section: Network Lifetime Design Objectivementioning

confidence: 99%

“…The earliest time instant at which any of the sensor nodes in the network fully depletes its battery [8]- [13]. NL-2…”

Section: Nl-1mentioning

confidence: 99%

“…More explicitly, the time-span until the specific instant, when the first data collection failure occurred. NL- 13 The longest time period, over which the QoS constraint, such as the signal-to-noise ratio (SNR) requirement is satisfied [30].…”

Section: Nl-1mentioning

confidence: 99%

“…A heuristic algorithm was proposed for solving the NL maximization problem at a reduced complexity, albeit this was achieved at the cost of a small reduction in NL compared the optimal NL solution. On the other hand, in [13] we proposed a two-stage NL maximization technique, where the NL was dependent on the SN's residual battery, since the SN's information was transmitted to a DN over alternative routes of a highcomplexity fully-connected WSN. Each route used for the transmission of the SN's information was computed for its route lifetime (RL) and these RL values were computed relying on the residual battery levels that were summed in order to determine the overall NL, until the SN's battery was completely depleted.…”

Section: A Conclusionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

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552

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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