Link quality ranking: Getting the best out of unreliable links

Zúñiga, Marco; Irzynska, Izabela; Hauer, Jan-Hinrich; Voigt, Thiemo; Boano, Carlo Alberto; Roemer, Kay

doi:10.1109/dcoss.2011.5982169

Cited by 19 publications

(8 citation statements)

References 23 publications

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“…A significant number of link quality estimation methods have been developed over the years. However, most of the current link quality estimation methods display low accuracy rates for evaluating link quality [10,12]. Thus because the link quality metrics are usually imprecisely estimated, it can be a challenge to distinguish the link quality around thresholds [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Huang

Por

Ang

et al. 2019

Advances in Fuzzy Systems

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Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a method that uses fuzzy logic to combine both hardware-based and software-based metrics is proposed to improve the accuracy rate for evaluating a link quality. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. The Fuzzifier module is used to determine the degree to which input link quality metrics belong to each fuzzy set through proposed membership functions. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier module. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The result from the Defuzzifier module is then used to evaluate the link quality. A simulation conducted to compare the accuracy rates of the proposed method and those found in related works showed that the proposed method had higher accuracy rates for evaluating a link quality.

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Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Huang

Por

Ang

et al. 2019

Advances in Fuzzy Systems

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“…On-Demand TPC (ODTPC) is designed to eliminate the bootstrapping phase required by algorithms like PCBL and ATPC, while reducing energy consumption and providing good link quality [19]. ODTPC is based on the assumption that there is a threshold in RSSI that separates good links from intermediate or bad links [27].…”

Section: Transmission Power Control Protocolsmentioning

confidence: 99%

Power Control in Wireless Sensor Networks with Variable Interference

Chincoli

Syed

Exarchakos

et al. 2016

Mobile Information Systems

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Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART) control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.

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“…Incorporating the RSSI into our approach is motivated by Zuniga et al's study, which aims at identifying the best of a set of unreliable links [26]. They show that RSSI and LQI are better estimators than PRR when only a small number of packets are used for link ranking.…”

Section: Related Workmentioning

confidence: 99%

Understanding Link Dynamics in Wireless Sensor Networks with Dynamically Steerable Directional Antennas

Voigt

Mottola

Hewage

2013

Lecture Notes in Computer Science

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Abstract. By radiating the power in the direction of choice, electronicallyswitched directional (ESD) antennas can reduce network contention and avoid packet loss. There exists some ESD antennas for wireless sensor networks, but so far researchers have mainly evaluated their directionality. There are no studies regarding the link dynamics of ESD antennas, in particular not for indoor deployments and other scenarios where nodes are not necessarily in line of sight. Our long-term experiments confirm that previous findings that have demonstrated the dependence of angleof-arrival on channel frequency also hold for directional transmissions with ESD antennas. This is important for the design of protocols for wireless sensor networks with ESD antennas: the best antenna direction, i.e., the direction that leads to the highest packet reception rate and signal strength at the receiver, is not stable but varies over time and with the selected IEEE 802.15.4 channel. As this requires protocols to incorporate some form of adaptation, we present an intentionally simple and yet efficient mechanism for selecting the best antenna direction at run-time with an energy overhead below 2% compared to standard omni-directional transmissions.

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Link quality ranking: Getting the best out of unreliable links

Cited by 19 publications

References 23 publications

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Power Control in Wireless Sensor Networks with Variable Interference

Understanding Link Dynamics in Wireless Sensor Networks with Dynamically Steerable Directional Antennas

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