Robust topology management in DT-MANETs: An efficient tree-based decentralized and multi-objective approach

Delay-tolerant mobile ad hoc network (DT-MANETs) feature frequent and long duration partitioned MANETs. It is a challenged environment where end-to-end connectivity cannot always be obtained. Furthermore, communication in such network is heavily relying on collaboration between nodes since there is no central authority. Limited resources of communication nodes present another facet of problems in such network. Moreover, the communication is typically done using wireless technologies which are sharing among communication nodes. In order to provide a better quality of service in such environment, topology management technique is used to help controlling network topology. The aim is to manage network resource and to enhance efficiency of communication. In this work, we proposed to do it by constructing an efficient and robust tree-based topology. We model the environment using dynamic and partitioned graph. Under such circumstances, protocols must withstand topology and condition changing. In summary, doing topology management in DT-MANETs encounters the following issues: cooperation among nodes, limitation of resources of mobile node, sharing medium, dynamic and partitioned topology and unrealistic and unscalable approach of centralized algorithm in such environment. This study focuses on managing tree-based topology in DT-MANETs. A set of active links is managed such that the deterioration of network is avoid and the quality of service in DT-MANETs is enhanced. Efficiency and robustness metrics are proposed accordingly. This work proposed to use different quality criteria based on communication node and edge for the selection topology. Both single- and multi-objectives tree-based topology are studied. Algorithms for managing tree-based topology are proposed according to different objectives. All proposed algorithms are purely decentralized, asynchronous algorithm and use one-hop information

A Study of Token Traversal Strategies on Tree-Based Backbones for Mobile Ad Hoc - Delay Tolerant Networks

International audienceTree-based backbone establishment and maintenance in Mobile ad hoc Delay Tolerant Networks is often operated throught the use of traversing tokens. A study and framework are proposed here for various token traversal strategies on tree-based backbones. The proposed strategies execute in a distributed and purely decentralized manner, and require only 1-hop knowledge. Aiming at providing the highest robustness and quality of services, these token-traversal strategies are studied in particular with an algorithm for merging and maintaining the different trees based on the quality of the nodes. For the robustness aspect, the use of a trust-based evaluation framework is assumed and the weights of the different nodes are based on their quality of cooperation. Three cost functions are implemented in order to evaluate the trust based framework proposed, including another function for evaluating tree-convergence time. Results and comparison charts are provided to illustrate the trade-off between the various strategies in terms of performances, cost (memory and communication) and robustness

Transforming Collaboration Data into Network Layers for Enhanced Analytics

We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental patterns and insights of the research collaborations. In this paper, we describe our data-to-networks method, which automatically generates a set of logical network layers from the relational input data using a linkage threshold. We, then, use a series of network metrics to analyze the impact of the linkage threshold on the individual network layers. Moreover, results from the network analysis also provide beneficial information to improve the network visualization. We demonstrate the feasibility and impact of our approach using real-world collaboration data. We discuss how the produced network layers can reveal insights and patterns to direct the data analytics more intelligently