Traffic management strategy for delay-tolerant networks

The Impact of Message Replication on the Performance of Opportunistic Networks for Sensed Data Collection

“…VACCINE [54] utilizes anti-packets to inform nodes to drop delivered messages. Shin et al's [55] message dropping policies are based on the total number of nodes in the network and the total number of replicas of a given message existing in the network. The former is assumed to be known, while the latter is estimated by exchanging and updating knowledge upon encounters.…”

“…The metadata overhead incurred by some replication techniques increases in higher node population. For example, the performance of Shin et al's [55] replication technique depends on a mechanism for deleting delivered messages. This requires nodes to exchange metadata in the form of lists containing IDs of delivered messages and expiration times.…”

“…Poor adaptability to increased network dynamicity Increased network dynamicity (i.e., more rapid and significant changes in network conditions such as topology, node density and encounter rates) in higher node population may also prevent some replication techniques from performing as expected, especially in the absence of global knowledge. Shin et al [55] focus on scenarios in which the number of nodes in the network is known (e.g., battlefield scenarios). However, this is not applicable to OppNets for sensed data collection in which the number of nodes increases without prior notification.…”

“…Hence, some message replication techniques (e.g., Lo et al [49] and Shin et al [55]) are complemented with buffer management policies, which often consist of rules for queuing messages, dropping messages or both. In order to create room in node buffers, most replication techniques utilize the first-in-first-out (FIFO) dropping policy in which the message that was received first is dropped first, while others drop messages with higher hop count (e.g., Lo et al [49]) or less remaining TTL (e.g., Shin et al [55]). The idea is that there is a higher probability of having more copies of such messages in the network and dropping them is unlikely to impact significantly on their delivery.…”

“…count (e.g., Lo et al [49]) or less remaining TTL (e.g., Shin et al [55]). The idea is that there is a higher probability of having more copies of such messages in the network and dropping them is unlikely to impact significantly on their delivery.…”