# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 2 Related Work
### Link Quality Estimators  
Link Quality Estimators (LQEs) such as LQI (Link Quality Indicator), RSSI (Received Signal Strength Indicator), and SNR (Signal-to-Noise Ratio) are considered hardware based, because they are directly read from the transceiver, not requiring any additional computation. However, as shown in previous studies, hardware based estimators are inaccurate [10]. Software based estimators enable the count of the reception ratio or the average number of packet transmissions/re-transmission. Some of the estimators explained in [10] are presented next.  
PRR (Packet Reception Ratio) counts the average number of packet retransmissions required before a successful reception. It is recommended for applications requiring low complexity level with moderate performance. Expected Transmission Count (ETX) tries to estimate the number of transmissions that are necessary to send a packet successfully. The number of packets received within a fixed window is compared to the number of packets expected, only updating the value at the end of each window. Four-Bit approximates the average number of packet transmissions/re-transmissions before a successful reception, but it heavily depends on the tuning of its parameters.  
Fuzzy Link-Quality Estimator (F-LQE), proposed by [PERSON] et al. [11], uses SNR, PSR (Packet Success Ratio), link asymmetry level (ASL), and stability (SF). The evaluation of the fuzzy rule returns the membership of the link in the fuzzy subset of good links. However, fuzzy methods incur a loss of precision despite being faster. Holistic Packet Statistics (HoPS), proposed by [PERSON] et al. [12], calculates four distinct quality metrics, describing the short- and long-term quality of a link at the same time, while also providing information about the dynamics of a link by means of the variation and trend of the link-quality. However, it is only possible to predict a useful link quality if the link behavior is studied in more detail. Wavelet-Neural-Network-based Link Quality Estimation (WNN-LQE) was presented by [PERSON] et al. [13] and uses SNR and PRR, but has a complex process for the estimation, not making it easier to implement in WSN nodes.  
In wireless networks, a routing protocol should select the shortest path with the most reliable paths [14], minimizing the link layer transmissions for a packet to be delivered and acknowledged. [PERSON] et al. [15] demonstrated that, using a default value for new neighbors tends to favor these neighbors among other connections; thus, the link quality of a connection has to be continuously estimated.  
Our network is used for navigation, environmental data gathering, and dissemination purposes, being considered a dense network. Therefore, an active monitoring approach may not be feasible, due to load and latency increase. Due to hardware characteristics, the overhearing technique is expensive, because the node must capture all the packets in the network in order to retrieve the RSSI of the received frame, and also time consuming since the wireless interface must be in monitoring mode. Therefore, our proposal is to use a passive monitoring for link quality estimator, without increasing collisions, network overhead and energy consumption.