Fuzzy Logic Based Multidimensional Link Quality Estimation for Multi-Hop Wireless Sensor Networks

Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine

“…Peng et al [13] propose a Multiple time scales link estimation (MTSLE) method by considering long and short time scales link. Guo et al [14] present a Fuzzy logic based link quality indicator (FLI), which reflects single hop reliability of packet delivery, link volatility, and packet loss burst. But in this method the obtaining of input parameters costs lots of energy in sending probe packets and calculation.…”

“…Comparing Table 3 with Table 4, it can be obviously found that the radial basis kernel function is superior and more suitable for the small sample of the SVM model. Finally, the accuracy of link quality estimation model proposed in this paper is compared with the ones of LQI-PRR [12] and FLI [14] models. Experiments have been conducted with the same experimental data in the same experimental scenario.…”

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

“…The challenge lies in an accurate estimation of the intermediate link quality. An ideal link quality estimator should be [10] energy efficient (i.e., requiring low processing, communication and memory overhead), accurate (i.e., reflecting the real link behavior), reactive (i.e., able to promptly react to persistent link state changes) and stable (i.e., able to tolerate transient link state changes). To better reflect the real behavior of a link, several link properties should be taken into account . That is why link quality estimators tend to combine several simple estimators [14] and use sophisticated techniques (e.g., simple average, filtering, machine learning [15,16], regression, and fuzzy logic [17,18]) to produce a metric from link measurements.…”

“…To better reflect the real behavior of a link, several link properties should be taken into account . That is why link quality estimators tend to combine several simple estimators [14] and use sophisticated techniques (e.g., simple average, filtering, machine learning [15,16], regression, and fuzzy logic [17,18]) to produce a metric from link measurements.…”

“…Link quality estimators have been used to improve the quality of service provided to end-users [19]. By using links of better quality, the network throughput is maximized, the delivery times are minimized, routes are more stable [18], etc. These improvements can be increased by link quality prediction using online machine learning techniques as in [20].…”