A multi-objective optimization of data dissemination in delay tolerant networks

Abstract

Data dissemination in delay tolerant networks is an important issue due to the complexity of a multi-hop network formed by a high number of nodes with limited resources, variable and unpredicted mobility conditions. Nodes have to act as expert systems and make suitable forwarding decisions based on local knowledge on the fly. Most of the proposed algorithms rely on adjusting a range of decision variables related to social and topological aspects of the network. Adjusting such parameters is still an open issue since many of them are interrelated. To solve this problem, we propose a multi-objective evolutionary simulation framework for optimizing in terms of delivery hit, delivery cost and latency, a probabilistic data dissemination algorithm based on well-known and widely used social and topological parameters such as centrality, similarity, social strength, friendship, and trust. The proposed multi-objective based optimization framework provides many advantages with respect to existing approaches based on single objective optimization. Primarily, it allows the network designer to have a complete view of the possible outcomes of the data dissemination algorithm through the Pareto front (non-dominated solutions). Furthermore, we propose a decision tree-based selection to obtain under which values of the decision variables we can find a set of solutions that meet a target performance. We validate this selection mechanism by providing the conditions under which we can find balanced solutions in the considered simulation scenarios. The solutions provided by the proposed approach have significant implications for the design of new data dissemination algorithms in DTNs.