Mobile Opportunistic Networks (MONs) are characterized by intermittent connectivity with long isolation period, and nodes following redundant transmissions for reliable message delivery. This often leads to unnecessary buffer occupancy, preventing new messages from getting replicated due to small contact duration and low bandwidth, or leading to packet drop under constrained buffer. Although attempts have been made to mitigate buffer congestion, the existing schemes are localized, are slow to react, or are specific to a routing scheme. Moreover, they rely on message exchanges to obtain buffer state/occupancy, thereby incurring additional overhead. In this paper, we first develop a generalized probabilistic forwarding model where the forwarding probability denotes the likelihood of a message to get forwarded to the encountered node. Based on the forwarding probability, we develop a congestion indicator and predict the point of congestion using the Kalman filter. Using this, a node can decide the optimal number and the exact set of messages to replicate, which leads to an optimal performance with minimal packet drop and overhead. Simulation results using a synthetic mobility model and a real-life mobility trace show that the proposed scheme outperforms the existing schemes.

Mobile opportunistic networks (MONs) are characterized by frequently changing network topology that rely on multi-copy transmission schemes to ensure the delivery of messages. However, the limited buffer capacity of mobile nodes leads to buffer overflow and head-of-line blocking. Head-of-line-blocking is caused due to short contact duration, which causes the older messages to be replicated at a faster rate than the newer messages waiting behind them. This unfair replication decreases the message delivery ratio and increases the average delivery delay. For implementation, we categorize a message as new or old and calculate the necessary maximum number of replicas required to deliver a message to the destination. If the instantaneous number of replicas in the network is less than this number, we schedule a message for replication and vice-versa. We demonstrate that our proposed scheme achieves a delivery ratio comparable to a utility-based optimal message drop algorithm with significantly lower overhead.

Due to the unavailability of an end-to-end path between the source and the destination, nodes in Mobile Opportunistic Networks (MON) follow a replication based strategy for message delivery. Such replications occur in bulks during intermittent and very short contact events, which often lead to buffer congestion in the relay nodes, thereby affecting the network performance. Older messages (those which have already been delivered to the destination) also tend to stay in the network for a longer time period hindering the spread of newly generated messages. Earlier works have considered buffer management techniques through local information exchange, which leads to overhead. In this work, an expression for the expected buffer occupancy under a given network scenario is obtained using the notion of a classical balls-into-bins problem. Thereafter, an estimator to estimate the buffer occupancy is designed, which can be directly used in buffer management algorithms without local information exchange. We compare the theoretical model, with estimated and simulated results, to prove the correctness of the model.

In Mobile Opportunistic Networks (MON), messages are transferred in bulk during a contact event. Such bulk transfers may cause a sudden increase in buffer occupancy, which may lead to congestion, and hence impact network performance. Apriori information of bulk transfer size distribution may help in taking proactive measures to prevent the onset of congestion. Therefore, we study the characteristic of bulk transfer size (BTS) and its impact on buffer occupancy. For this, we simulated the network using synthetic and real-life mobility traces and found that the aggregate inter-contact time (ICT) and aggregate BTS can be well approximated by the lognormal distribution. Further, we devised an estimator for mean BTS, which can be used by a receiving node to decide whether to accept or reject the bulk in order to avoid buffer overflow. We also found that an increase in expected ICT leads to an increase in expected BTS, which is confirmed using a simple linear regression model.