WCOT: A utility based lifetime metric for wireless sensor networks

Özgövde, Atay; Ersoy, Cem

doi:10.1016/j.comcom.2008.11.024

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…Although most of existing works are effective to estimate FNDT, the period from FNDT to the time when all the sensor nodes are dead or the network is completely disabled (i.e., All Node Died Time, ANDT) is relatively long [13]. For most applications, a small portion of dead nodes may not cause a network failure, although they can impact the network performances [12]- [14].…”

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confidence: 99%

“…Recently, increasing attention has been paid to the period from FNDT to ANDT and analyzing the entire network lifetime [7], [13]- [15]. Ozgovde et al [13] highlight that FNDT is only an important stage of the entire network lifetime and ANDT is an important factor to evaluate the network performance.…”

mentioning

confidence: 99%

“…Ozgovde et al [13] highlight that FNDT is only an important stage of the entire network lifetime and ANDT is an important factor to evaluate the network performance. To this end, they propose a utility based lifetime measurement framework called Weighted Cumulative Operational Time (WCOT), which calculates a network lifetime function based on the complete history of the network states.…”

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confidence: 99%

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Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Ren

Zhang

et al. 2016

IEEE Trans. Ind. Inf.

203

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Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole, and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.Index Terms-wireless sensor network, network lifetime, energy hole, energy efficiency, routing.

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Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Ren

Zhang

et al. 2016

IEEE Trans. Ind. Inf.

203

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“…However, this metric may not be precise when the running application tolerates a determined number of packet losses or a determined delay (Ozgovde and Ersoy, 2009). Thus, the lifetime of the network should be application-specific, and hence each different application must be studied to determine if the first node energy depletion is enough to characterize the lifetime of the WSN.…”

Section: Drivers For Cross-layer Approachesmentioning

confidence: 98%

A survey on cross-layer solutions for wireless sensor networks

Mendes

Rodrigues

2011

Journal of Network and Computer Applications

139

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“…A sliding window is set up to adjust the electing probability and manage to keep stable the expected number of the cluster heads, and this method can balance the energy consumption and extend the network lifetime better [14]. To correctly quantify the lifetime, a utility based lifetime measurement framework called Weighted Cumulative Operational Time (WCOT) is proposed [15]. LEACH is analyzed for mobility, and the model evaluates data loss after construction of the clusters due to node mobility, and sets a topology update interval to balance the energy and data loss ratio [16].…”

Section: Introduction Leach (Low Energy Adaptive Clustering Hierarmentioning

confidence: 99%

LEACH-G: an Optimal Cluster-heads Selection Algorithm based on LEACH

Chen¹,

Zhang²,

Zong³

et al. 2013

JSW

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As a classic clustering algorithm, LEACH is widely used in wireless sensor networks. Due to the random cluster head selection process, LEACH does not guarantee optimization for the number and position of cluster heads. In order to further optimize LEACH, an optimal energy-efficient cluster-heads selection algorithm LEACH-G for LEACH protocol is presented. We calculate the optimal number of cluster heads based on energy model for LEACH algorithm, and further set forth on LEACH-G algorithm for optimal cluster heads. We use NS2 simulation platform to compare LEACH-G with LEACH. By calculating the optimal number of cluster-heads, the energy consumption of the sensor nodes in LEACH-G algorithm can be more evenly distributed in the wireless sensor networks to avoid too much energy consumption of single node to untimely death, which affect the network life cycle. Compared with LEACH protocol, the simulation results demonstrate that LEACH-G algorithm can effectively reduce the energy consumption and increase the system lifetime.

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WCOT: A utility based lifetime metric for wireless sensor networks

Cited by 10 publications

References 21 publications

Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

A survey on cross-layer solutions for wireless sensor networks

LEACH-G: an Optimal Cluster-heads Selection Algorithm based on LEACH

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