Data Offloading via Optimal Target Set Selection in Opportunistic Networks

The rapid rate of dependence over internet usage using digital devices also results in enormous data traffic. The conventional way to handle these services is to increase the infrastructure. However, it results in high cost of implementation. Therefore, to overcome the data burden, researchers have come up with data offloading schemes using solutions for NP-hard Target Set Selection (TSS) problem. Our work focuses on TSS optimization and respective data offloading scheme. We propose a heuristics-based optimal TSS algorithm, a distinctive community identification algorithm, and an opportunistic data offloading algorithm. The proposed scheme has an overall polynomial time complexity of the order O(k(3)), where k is the number of nodes in the primary target set for convergence. However we have obtained its realization to linear order for practical reasons. To validate our results, we have used state-of-the-art datasets and compared it with literature-based approaches. Our analysis shows that the proposed Final Target Set Selection (FTSS) algorithm outperforms the greedy approach by 35% in terms of traffic over cellular towers. It reduces the traffic by 20% as compared to the heuristic approach. It has 23% less average latency in comparison to the community-based algorithm.