An Evolutionary Approach to Multi-point Relays Selection in Mobile Ad Hoc Networks

Singh, Alok; Bhukya, Wilson Naik

doi:10.1007/978-3-030-34869-4_41

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem of global minimization of the MPR set size has been formulated as an integer linear programming (ILP) that can be solved efficiently for relatively small or medium-size networks [8]. The MPR set selection algorithm is formulated as ILP in [8], [9] or as genetic algorithm [10]. However, the global minimum is not practical as it requires centralized coordination, and high computational power for large networks, and therefore not scalable.…”

Section: A Related Workmentioning

confidence: 99%

DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks

Kaviani

Ryu

Ahmed

et al. 2021

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)

View full text Add to dashboard Cite

Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.

show abstract