Research Data: Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks

This DOI contains the datasets of Figures 7,8,12-17 of the paper titled "Quantum-Assisted Joint Multi-Objective Routing and Load Balancing for Socially-Aware Networks". Each folder is named according to the corresponding figure, where the dataset of each curve is stored in a .dat file. To regenerate the figures please use the command "gle Figure_Name.gle" (Graphics Layout Engine -GLE- should be installed on your machine).

Paper Abstract:
The widespread use of mobile networking devices, such as smart phones and tablets, has substantially increased the number of nodes in the the operational networks. These devices often suffer from the lack of power and bandwidth. Hence, we have to optimize their message-routing for the sake of maximizing their capabilities. However, the optimal routing typically relies on a delicate balance of diverse and often conflicting objectives, such as the route's delay and power consumption. The network design also has to consider the nodes' user-centric social behavior. Hence, the employment of socially-aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network's packet-flow. In this treatise, we propose a novel algorithm, referred to as the \emph{Multi-Objective Decomposition Quantum Optimization} (MODQO) algorithm, which exploits the Quantum Parallelism to its full potential by reducing the database correlations for performing multi-objective routing optimization, while at the same time balancing the tele-traffic load among the nodes without imposing a substantial degradation on the network's delay and power consumption. Furthermore, we introduce a novel socially aware load balancing metric, namely the normalized entropy of the normalized composite betweenness of the associated socially-aware network, for striking a better trade-off between the network's delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full-search. Finally, we compare the MODQO algorithm to the classic NSGA-II evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network's average delay, whilst simultaneously reducing the network's average power consumption by 6 dB without increasing the computational complexity.