F-LQE: A Fuzzy Link Quality Estimator for Wireless Sensor Networks

Baccour, Nouha; Koubâa, Anis; Youssef, Habib; Jamâa, Maissa Ben; Rosário, Denis; Alves, Mário; Becker, Leandro Buss

doi:10.1007/978-3-642-11917-0_16

Cited by 119 publications

(118 citation statements)

References 14 publications

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“…However, these models are relatively slow as they require a few packets for building up meaningful packet statistics (typically more than ten packets). Hybrid models combining the above approaches [9][10][11] have shown to further improve the accuracy. Yet, the estimation time remains high as a few packets are still required to build up a reliable link estimation.…”

Section: Introductionmentioning

confidence: 99%

BLITZ: Wireless Link Quality Estimation in the Dark

Spuhler

Lenders²,

Giustiniano

2013

Lecture Notes in Computer Science

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Abstract. We present BLITZ, a novel link quality estimator that relies on physicallayer synchronization errors to estimate the expected packet delivery ratio of wireless links. In contrast to all existing link quality estimators which estimate the packet delivery based on statistics from packets that are successfully decoded, our technique works even when packets at the receiver are not correctly received, i.e., when the synchronization fails. The core idea of BLITZ is to exploit information from chip errors in the received preamble of any transmitted direct sequence spread spectrum signals such as IEEE 802.15.4. Using extensive measurements over cable, wireless static and wireless mobile scenarios, we show that our proposed estimator outperforms existing estimators in terms of both accuracy and speed. Across diverse environmental conditions and the full range of possible link qualities, BLITZ provides packet delivery ratio estimates with an absolute error below six percent within just a few milliseconds.

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Section: Introductionmentioning

confidence: 99%

BLITZ: Wireless Link Quality Estimation in the Dark

Spuhler

Lenders²,

Giustiniano

2013

Lecture Notes in Computer Science

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“…RSSI and LQI) set by the radio driver after packet reception. Software based link estimators such as Four bit [7] and Fuzzy LQE [4], combine information from multiple network layers into a single metric. There are also very simple link estimators, such as the objective function 0, that select neighbours based on hop count.…”

Section: Link Estimatorsmentioning

confidence: 99%

“…It uses LQI and RSSI values to account link quality and combines this with the ETX path metric. Fuzzy logic [4] combines multiple link metrics in a less deterministic way by using membership functions which assign a score in [0, 1] for every link metric. These individual scores are combined with an aggregation function such as the Yagger operator.…”

Section: Software Based Link Estimation Algorithmsmentioning

confidence: 99%

An Evaluation of Link Estimation Algorithms for RPL in Dynamic Wireless Sensor Networks

Ruckebusch

Devloo

Carels

et al. 2016

Internet of Things. IoT Infrastructures

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Abstract. Link estimators are extremely important in dynamic wireless sensor networks for obtaining a good network performance because they drive the decisions made by the routing protocol. Many estimators exist but the quality of their estimation depends on the scenario at hand. In this paper, the impact of the estimator on the network performance is investigated in different networking scenarios. Also the influence of the underlying MAC protocol was evaluated. The evaluation was performed both in simulation and on a real-life testbed. The results clearly show that there is no link estimator that is best in all scenario's. Another major finding indicates that the results obtained in simulator differ heavily from the testbed results. This illustrates that research findings cannot be solely based on simulation results but also requires real-world experiments. The influence off the underlying MAC protocol is limited on the choice of estimator.

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“…The signal strength is suggested as a good measure of potential connectivity, while data throughput should be used for ensuring minimum actual data transmission rate. The authors of [5] and [6] conclude that the link estimation is more efficient when several link quality metrics are combined together. However, as explained in survey of Baccour et al [4], high overhead (memory footprint, computation time) are still open problems, when several link properties are used.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Restoration and Amelioration in Wireless Ad-Hoc Networks: A Practical Solution

Katsikiotis

Zorbas

2014

Ad Hoc Networks

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Connectivity restoration after a node failure is one of the major issues in wireless ad-hoc networks. In particular, failures can lead to a network partitioning and a huge loss of information. Therefore, a fast mechanism is needed to heal the network between the partitions. In this paper, we consider the scenario where an intermediate node failures and a mobile system is moving autonomously to restore connectivity and provide the best service. We propose a fast connectivity restoration algorithm that is based only on local information. We implement our solution on a real robotic platform and we present some experimental results using a simple case scenario.

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F-LQE: A Fuzzy Link Quality Estimator for Wireless Sensor Networks

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Cited by 119 publications

References 14 publications

BLITZ: Wireless Link Quality Estimation in the Dark

BLITZ: Wireless Link Quality Estimation in the Dark

An Evaluation of Link Estimation Algorithms for RPL in Dynamic Wireless Sensor Networks

Connectivity Restoration and Amelioration in Wireless Ad-Hoc Networks: A Practical Solution

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