Using Novel Distributed Heuristics on Hexagonal Connected Dominating Sets to Model Routing Dissemination

摘要

Network-centric Future Force must support a large and diverse group of communication nodes. Despite the fact that there is ample scope for network design in networks such as mesh or MANETs, there are important performance limits when network conditions become more extreme (highly mobile or dense). In previous work, we modeled and analyzed the expected user performance of a number of novel approaches to flooding link state routing information in wireless ad hoc networks for routing update. We compared routing dissemination schemes such as this of Flat Flooding and Multi-Point Relays (MPRs), with our own variations based on Connected Dominating Sets (CDSs), assuming other parts of the routing protocol were taken from standard Link State Routing (OLSR) in the former case or Open Shortest Path First (OSPF) in the latter case. Although the existing literature provides a variety of models for Flat Flooding and MPRs, it lacks similar analytical work for relays placement under CDSs. Towards the latter, we selected one representative novel CDS-based model ȁ3; the CDS-Hexagon, as it provided the lowest routing overhead among other properties. Our analysis demonstrated the difference in the impact of conditions on key performance metrics, such as this of network density on routing overhead as well as a tradeoff between routing overhead and routing stretch. Then, we upgraded CDS-HEX dissemination from the limited-scope centralized scenarios with symmetrical relays placement to dynamic ones with totally random relay placement. We used a novel heuristic to approximate the theoretically optimal CDS-HEX for dynamic environments. Our scheme in one hand is not overly expensive to set up despite the more complex generation process, and on the other hand has a superior performance for the majority of network conditions, close to the optimal anticipated by the corresponding centralized model. In this work, we provide far more details, analysis and insight on our heuristic. Moreov--er, we expand our simulations to measure additional metrics of interest such as routing stretch and routing redundancy.