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(k³), 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.

使用数字设备对互联网使用的快速依赖性也导致了巨大的数据流量。处理这些服务的常规方法是增加基础结构。但是，这导致了高昂的实施成本。因此，为了克服数据负担，研究人员提出了使用NP硬目标集选择（TSS）问题解决方案的数据卸载方案。我们的工作集中在TSS优化和相应的数据卸载方案上。我们提出了一种基于启发式的最优TSS算法，一种独特的社区识别算法以及一种机会主义的数据卸载算法。所提出的方案的整体多项式时间复杂度为O（k ³），其中k是要收敛的主要目标集中的节点数。但是，出于实际原因，我们已将其实现为线性顺序。为了验证我们的结果，我们使用了最先进的数据集，并将其与基于文献的方法进行了比较。我们的分析表明，就蜂窝塔上的流量而言，建议的最终目标集选择（FTSS）算法的性能优于贪婪方法的35％。与启发式方法相比，它将流量减少了20％。与基于社区的算法相比，它的平均延迟减少了23％。