![]() ![]() Therefore any proposed method ought to take into account the varying characteristics of hardware- and software-based metrics. However, using only software-based metrics to evaluate a link quality cannot capture link quality changes in real time. ![]() On the other hand, software-based metrics can evaluate the link quality more accurately than hardware-based metrics. For example, hardware-based metrics evaluations can reflect changes in link quality in real time. Different types of link quality metrics have different characteristics. Software-based metrics are obtained through a calculation that is made in accordance with the received packet statistics, for instance, Packet Reception Ratio (PRR). Hardware-based metrics are acquired directly from radio transceivers, with some examples being Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), and Signal to Noise Ratio (SNR). There are two types of link quality metrics: hardware-based and software-based. Link quality is a significant factor that affects transmission rates of sensor nodes and link quality metrics are used to evaluate a link quality. These sensor nodes normally have limited power supply due to their small sizes, and their transmission rates are affected by many factors. WSN relies on the sensor nodes to transmit packets. WSN is widely applied in many sectors such as in military, industry, and environmental monitoring. These sensor nodes are used to monitor the physical conditions of the environment and transmit the collected data to a sink. IntroductionĪ wireless sensor network (WSN) is a wireless network that is composed of a group of spatially dispersed sensor nodes with a radio transceiver. 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. The result from the Defuzzifier module is then used to evaluate the link quality. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier 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. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. 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. 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. Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. ![]()
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