Network topology discovery describes the physical structure of the network, which in turn reflects the purpose of this network design. Used tools and techniques vary depending on that structure in order to choose the optimal methods for implementing the appropriate distributed applications. Mobile agents have become a focus of attention for implementing distributed management architectures because of their autonomy, flexibility and ability of asynchronous communication, while centralized models affect on the network performance such as latency, throughput and jitter. The main objective of this study is to discuss the use of mobile agent for discover the network topology either by traditional methods, protocols or by graphic theory, Genetic and Swarm algorithms that belong to the artificial intelligence filed and compare between them. As a result, identify the appropriate technique for extracting the network management information to obtain optimal itineraries for mobile agents.

using multi agents in the wireless sensor networks (WSNs) for aggregating data has gained significant attention. Planning the optimal itinerary of the mobile agent is an essential step before the process of data gathering. Many approaches have been proposed to solve the problem of planning MAs itineraries, but all of those approaches are assuming that the MAs visit all SNs and large number of intermediate nodes. This assumption imposed a burden; the size of agent increases with the increase in the visited SNs, therefore consume more energy and spend more time in its migration. None of those proposed approaches takes into account the significant role that the connected dominating nodes play as virtual infrastructure in such wireless sensor networks WSNs. This article introduces a novel energy-efficient itinerary planning algorithmic approach based on the minimum connected dominating sets (CDSs) for multi-agents dedicated in data gathering process. In our proposed approach, instead of planning the itineraries over all sensor nodes SNs, we plan the itineraries among subsets of the MCDS in each cluster. Thus, no need to move the agent in all the SNs, and the intermediate nodes (if any) in each itinerary will be few. Simulation results have demonstrated that our approach is more efficient than other approaches in terms of overall energy consumption and task execution time.