In typical end-to-end recovery protocols, an ACK segment is delivered to a source node over a single path. ACK loss requires the source to retransmit the corresponding data packet. However, in underwater wireless sensor networks which prefer flooding-based routing protocols, the source node has redundant chances to receive the ACK segment since multiple copies of the ACK segment can arrive at the source node along multiple paths. Since existing RTO calculation algorithms do not consider inherent features of underlying routing protocols, spurious packet retransmissions are unavoidable. Hence, in this letter, we propose a new ACK loss-aware RTO calculation algorithm, which utilizes statistical ACK arrival times and ACK loss rate, in order to reduce such retransmissions.

With the advent of high speed links, online flow measurement for, e.g., flow round trip time (RTT), has become difficult due to the enormous demands placed on computational resources. Most existing measurement methods are designed to count the numbers of flows or sizes of flows, but we address the flow RTT measurement, which is an important QoS metric for network management and cannot be measured with existing measurement methods. We first adapt a standard Bloom Filter (BF) for the flow RTT distribution estimation. However, due to the existence of multipath routing and Syn flooding attacks, the standard BF does not perform well. We further design the double-deletion bloom filter (DDBF) scheme, which alleviates potential hash collisions of the standard BF by explicitly deleting used records and implicitly deleting out-of-date records. Because of these double deletion operations, the DDBF accurately estimates the RTT distribution of TCP flows with limited memory space, even with the appearance of multipath routing and Syn flooding attacks. Theoretical analysis indicates that the DDBF scheme achieves a higher accuracy with a constant and smaller amount of memory compared with the standard BF. In addition, we validate our scheme using real traces and demonstrate significant memory-savings without degrading accuracy.

The end-to-end round trip time (RTT) is one of the most important communication characteristics for Internet applications. From the viewpoint of network operators, RTT may also become one of the important metrics to understand the network conditions. Given this background, we should know how a factor such as a network incident influences RTTs. It is obvious that two or more factors may interfere in the observed delay characteristics, because packet transmission delays in the Internet are strongly dependent on the time-variant condition of the network. In this paper, we propose a modeling method by using mixed distribution which enables us to express delay characteristic more accurately where two or more factors exist together. And, we also propose an inferring method of network behavior by decomposition of the mixed distribution based on modeling results. Furthermore, in experiments we investigate the influence caused by each network impact factor independently. Our proposed method can presume the events that occur in a network from the measurements of RTTs by using the decomposition of the mixed distribution.