Quick and Efficient Link Quality Estimation in Wireless Sensors Networks

Audéoud, Henry-Joseph; Heusse, Martin

doi:10.23919/wons.2018.8311667

Cited by 27 publications

(45 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common wireless link quality estimators a) RSSI (Received Signal Strength Indicator): Upon packet reception, the radio chip provides a measure of the received signal power. Like many others [1], we have recognized in a previous paper [4] that high reception power implies good reception quality. However, it is hard to discriminate between good and bad links for a lower level of the received signal power.…”

Section: Related Worksupporting

confidence: 55%

“…We have previously studied the AT86RF231 radio chip on the IoT-lab testbed [4] and obtained encouraging results on the ability of LQI to predict the link PER. In Figure 1, the precision of the PER estimation is good on channel 26 (the inter-percentile interval is thin).…”

Section: Related Workmentioning

confidence: 98%

“…This fact limits the range of the RSSI values for which our method is applicable. However, a large fraction of packets are lost in this range of reception power [4], so that the majority of the corresponding links have a high PER and thus should be avoided except, as a last resort.…”

Section: Sensitivity Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

Single Reception Estimation of Wireless Link Quality

Audéoud

Heusse

Duda

2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

Self Cite

View full text Add to dashboard Cite

Quick and accurate estimation of link quality, and more specifically packet loss probability, is the key element for efficient and effective communications in wireless multi-hop networks. We focus on IEEE 802.15.4 and we posit that losses only occur when noise and interference last long enough and are strong enough relatively to the received signal, to hinder packet reception. So, the key information for any ordered node pair is the signal to noise plus interference ratio distribution, which we obtain by combining the observed noise plus interference power at the receiver with the received signal strength. In this paper, we propose two novel schemes for the estimation of PER: Burst-NISI and Sample-NISI. Burst-NISI is based on high frequency measurements of the power level of ambient noise and interference around a given node. Sample-NISI sporadically samples the power level of ambient noise and interference when the radio operates according to a duty cycle. Using a large scale experimental platform, we show that our packet error rate estimation schemes are accurate for any packet length and diverse experimentation sites with different settings, for which the prediction is within 10 percentage points of the PER value measured a posteriori.

show abstract

Section: Related Worksupporting

confidence: 55%

Section: Related Workmentioning

confidence: 98%

See 1 more Smart Citation

Single Reception Estimation of Wireless Link Quality

Audéoud

Heusse

Duda

2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, unlike [8], we consider the optimal solution for any flow to be the one that minimizes the total number of transmissions per message of this flow, while achieving the desired end-to-end reliability. Furthermore, traces obtained from real IoT networks like [24,25] allow us to better understand the challenges of wireless networking and make realistic assumptions. That is why, in this paper, the performance evaluation is based on PDR values computed from a realistic model, as explained in Section 6.1.…”

Section: Related Workmentioning

confidence: 99%

Optimal Number of Message Transmissions for Probabilistic Guarantee of Latency in the IoT

Minet

Tanaka

2019

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R % of messages generated by sensor nodes are delivered to the sink with a latency ≤L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. M F a i r is very easy to compute, whereas M O p t minimizes the total number of transmissions necessary for a message to reach the sink. M F a i r and M O p t are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions M a x T r a n s is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M a x T r a n s is fixed. For both TSCH networks, we show that M O p t provides a better reliability and a longer lifetime than M F a i r , which provides a shorter average end-to-end latency. M O p t provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler.

show abstract

“…NDN has been applied in different domains including Internet of Things [6], Vehicular Ad hoc Networks [7], and Wireless Sensor Network [8]. On the other hand, Link Quality Estimation (LQE) [9] is used to estimate and select the stable and reliable link over a set of candidate links that can be used to forward interest. LQE integrates different information and metrics to select the stable link.…”

Section: Introductionmentioning

confidence: 99%

LQCC: A Link Quality-based Congestion Control Scheme in Named Data Networks

Khelifi¹,

Luo²,

Nour³

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Information-Centric Networking (ICN) is a new communication paradigm that replaces the host addresses by the name of content; Named Data Networking (NDN) is a promising ICN architecture that has attracted research attention in recent years. NDN is a receiver-driven architecture implements pullbased communication in the form of one-interest-one-data. This model poses different challenges, especially from the transport layer perspective. In contact to IP-based networks where the congestion is handled in an end-to-end manner, NDN cannot apply the same concept, while most of the existing solutions are based on hop-by-hop connection. In this paper, we present a new congestion control mechanism for NDN based on link quality estimation. We focus our efforts to provide fast data transmission, decrease packet dropping rate, and maximize the link utilization. The simulation results show that our solution outperforms the NDN schemes in terms of throughput and drop packets.

show abstract

Quick and Efficient Link Quality Estimation in Wireless Sensors Networks

Cited by 27 publications

References 12 publications

Single Reception Estimation of Wireless Link Quality

Single Reception Estimation of Wireless Link Quality

Optimal Number of Message Transmissions for Probabilistic Guarantee of Latency in the IoT

LQCC: A Link Quality-based Congestion Control Scheme in Named Data Networks

Contact Info

Product

Resources

About