A Mobile Ad-hoc Network is a self-configured infrastructure-less network. It consists of autonomous mobile nodes that communicate over bandwidth-constrained wireless links. Nodes in a MANET are free to move randomly and organize themselves arbitrarily. They can join/quit the network in an unpredictable way; such rapid and untimely disconnections may cause network partitioning. In such cases, the network faces multiple difficulties. One major problem is data availability. Data replication is a possible solution to increase data availability. However, implementing replication in MANET is not a trivial task due to two major issues: the resource-constrained environment and the dynamicity of the environment makes making replication decisions a very tough problem. In this thesis, we propose a fully decentralized replication model for MANETs. This model is called CReaM: “Community-Centric and Resource-Aware Replication Model”. It is designed to cause as little additional network traffic as possible. To preserve device resources, a monitoring mechanism are proposed. When the consumption of one resource exceeds a predefined threshold, replication is initiated with the goal of balancing the load caused by requests over other nodes. The data item to replicate is selected depending on the type of resource that triggered the replication process. The best data item to replicate in case of high CPU consumption is the one that can better alleviate the load of the node, i.e. a highly requested data item. Oppositely, in case of low battery, rare data items are to be replicated (a data item is considered as rare when it is tagged as a hot topic (a topic with a large community of interested users) but has not been disseminated yet to other nodes). To this end, we introduce a data item classification based on multiple criteria e.g., data rarity, level of demand, semantics of the content. To select the replica holder, we propose a lightweight solution to collect information about the interests of participating users. Users interested in the same topic form a so-called “community of interest”. Through a tags analysis, a data item is assigned to one or more communities of interest. Based on this framework of analysis of the social usage of the data, replicas are placed close to the centers of the communities of interest, i.e. on the nodes with the highest connectivity with the members of the community. The results of evaluating CReaM show that CReaM has positive effects on its main objectives. In particular, it imposes a dramatically lower overhead than that of traditional periodical replication systems (less than 50% on average), while it maintains the data availability at a level comparable to those of its adversaries.
