## 2 Message Replication in OppNets for Sensed Data Collection
### Variable-Quota Replication  
There are message replication approaches that seek more flexibility by utilizing a variable quota. By forwarding a limited number of copies and monitoring certain message properties (e.g., hop count), replication can be controlled by switching to single-copy replication when a threshold is reached, e.g., EpiPRo [53]. Due to varying encounter opportunities among nodes and different messages being replicated at different rates, the resulting number of copies is not fixed but dynamically varies for each message. Gossiping [54], which is also referred to as Randomized Flooding, randomly selects the value of a parameter, \(p\), upon encounter. The parameter allows trade-offs between transmission overhead and throughput as it is varied in the range (0, 1). It dynamically switches between Epidemic and Direct Transmission such that, when it is 1, the message is replicated, and when it is 0, the message is retained unless the encountered node is the destination. [PERSON] et al. [54] also propose another means of controlling replication through a time threshold. With every message, a custodian associates a timer that, after exhaustion, prompts the message to be removed from the buffer. The only exception refers to messages generated by the custodian itself, which allows a certain level of delivery guarantee.  
There are strategies that rather attempt to alleviate the overhead caused by excessive replication. VACCINE [54] utilizes anti-packets to inform nodes to drop delivered messages. [PERSON] 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. [PERSON] and [PERSON] [56] rely on the Uniform Minimum Variance Estimator (UMVUE) for this estimate, which is based on determining the node population from a set of observed IDs of previously encountered nodes.  
Other strategies change replication approaches according to message properties, node connectivity, or perceived network conditions. [PERSON] and [PERSON]'s [57] adaptive spraying scheme replicatesmessages until a certain \"switch value\" is reached (which is based on the number of remaining quota, \(L\), and a chosen \"reference value\"), then shifts to single-copy replication. [PERSON] et al. [58] dynamically control the number of message copies based on the TTL. [PERSON] et al.'s [59] dynamic replication is based on the average hop count of all routes existing between the message custodian and the destination. The average hop count is determined by maintaining and updating information in message headers, and a message is replicated only when the relay node has a low probability of delivering the message within the average hop count.  
Due to intermittent connectivity, [PERSON] et al. [51] consider the connection strength between the source and destination, and determine the number of message copies as a fraction of a pre-determined value, \(L_{M}\). Thus, less connection strength implies more copies (i.e., a larger fraction of \(L_{M}\)), and vice versa. [PERSON] et al.'s [49] approach is based on the level of congestion, which is inferred from the state of buffers of encountered nodes. n-Epidemic Routing [60] replicates a message only when at least \(n\) nodes are in contact.