Prediction Accuracy of Link-Quality Estimators

Renner, Christian; Ernst, Sebastian; Weyer, Christoph; Turau, Volker

doi:10.1007/978-3-642-19186-2_1

Cited by 38 publications

(44 citation statements)

References 10 publications

(17 reference statements)

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“…LQ prediction is used in addition to LQ tracking to determine beforehand which links are more likely to change their behavior. Although LQ prediction is not identical with EtEQ prediction some of the above techniques can be very similar [5], [15], [22], [23], [25], [27]. This relation is even more direct in the case of ETX EtEQ, studied in this paper, which is a linear function of the ETX LQ.…”

Section: Link Quality Prediction In Community Networkmentioning

confidence: 77%

“…LQE with metrics like LQI (Link Quality Indication) [22], SNR (Signal-to-Noise Ratio) [23] or RSSI (Received Signal Strength Indication) [24] fit in the former category, whereas metrics like RNP (Required Number of Packets) [25], ETX (Expected Transmission Count) [15], [25] or PSR (Packet Success Rate) [5] fit in the latter. All these metrics can be used by LQE in isolation or as a combination of some of them [11], [12], [27] to select the more suitable neighbor nodes when making routing decisions. LQ prediction is used in addition to LQ tracking to determine beforehand which links are more likely to change their behavior.…”

Section: Link Quality Prediction In Community Networkmentioning

confidence: 99%

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Tracking and Predicting End-to-End Quality in Wireless Community Networks

Millan

Molina

Dimogerontakis

et al. 2015

2015 3rd International Conference on Future Internet of Things and Cloud

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Abstract-Community networks are an emergent model with mottos like "a free net for everyone is possible" or "don't buy the network, be the network". Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-toEnd quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of time-series analysis. We apply this prediction technique in the routing layer of largescale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.

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Section: Link Quality Prediction In Community Networkmentioning

confidence: 77%

Section: Link Quality Prediction In Community Networkmentioning

confidence: 99%

Tracking and Predicting End-to-End Quality in Wireless Community Networks

Millan

Molina

Dimogerontakis

et al. 2015

2015 3rd International Conference on Future Internet of Things and Cloud

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show abstract

“…In order to favor links with high delivery probability, link-quality estimates, such as LQI or RSSI, should be used to ignore beacons from neighbors offering a poor link [19]. We do not consider logical link metrics based on packet statistics, since they cannot be implemented in a stateless fashion, i.e., nodes must explicitly keep track of their neighbors and the corresponding link metric [17].…”

Section: Packet Transmission Phasementioning

confidence: 99%

“…Link-quality estimation techniques [6,17] have been proposed to tackle the problem of the fluctuating, unstable, and lossy nature of the wireless channel [22] in order to identify reliable communication partners. Low-power medium access protocols were designed to buy extended network lifetime at the cost of increased latency [12,20,5].…”

Section: Introductionmentioning

confidence: 99%

Opportunistic, Receiver-Initiated Data-Collection Protocol

Unterschütz

Renner

Turau

2012

Lecture Notes in Computer Science

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Abstract. This paper presents and evaluates ORiNoCo, a novel datacollection and event-reporting protocol for sensor networks. ORiNoCo is built upon the asynchronous duty-cycle protocol RI-MAC and breaks with the tradition of exchanging extensive neighborhood information, a cornerstone of many competing collection protocols and one of their major source of communication overhead and energy expenditure. The merit of this venture is an opportunistic, energy-efficient, latency-reducing, and self-stabilizing protocol. ORiNoCo comes at virtually no extra costs in terms of memory demand and communication overhead compared to RI-MAC. We derive theoretical boundaries for the improvements in radio efficiency, latency, and energy-consumption. ORiNoCo is verified with these findings via simulation and compared with CTP. ORiNoCo achieves lower energy-consumption while reducing end-to-end delays.

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“…Additional precision and positioning accuracy can be achieved by using other features besides LQI, such as both the short and long-term characteristics of the link quality, as well as information about dynamic variation and trends of the signal [17,18]. For example, in [18], four distinct quality metrics are used.…”

Section: Introductionmentioning

confidence: 99%

Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

Oliveira¹,

Raju²,

Agrawal³

2012

NPA

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The received signal strength indicator (RSSI) and the link quality indicator (LQI) are the metrics that are commonly available in commercial off-the-shelf (COTS) sensor hardware. The former has been widely regarded as the main source for distance estimation and node localization. However, experimentally RSSI has been shown to behave in an inconsistent manner, even in ideal scenarios, and serve at best as bounds for distances. The latter is effectively a measure of chip error rate, and can be used to identify higher quality transmissions, and the combination RSSI/LQI can be expected to make more precise estimates with the tradeoff of increased delay and estimation cost. In this paper, we describe our distance estimation system that uses these two metrics and test our hypothesis purely through experimental measurements using sensor nodes. Results indicate that such a combination of metrics can be used to provide a tighter bound on the range of estimated distances. We then quantify the improvement in distance estimation by relying on these two metrics. Through a unique classification using fuzzy logic and TBM, we developed an algorithm that is capable of precise distance estimation within the range of 100cm to 400cm, on at least 80% of the times while reaching accuracy as high as 100%.

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Prediction Accuracy of Link-Quality Estimators

Cited by 38 publications

References 10 publications

Tracking and Predicting End-to-End Quality in Wireless Community Networks

Tracking and Predicting End-to-End Quality in Wireless Community Networks

Opportunistic, Receiver-Initiated Data-Collection Protocol

Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

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