Sharma Prince, Shukla Shailendra, Vasudeva Amol
Jaypee University of Information Technology, Waknaghat, Solan,, Himachal Pradesh PIN 173234 India.
Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh PIN 211004 India.
Mob Netw Appl. 2021;26(3):1270-1280. doi: 10.1007/s11036-021-01760-2. Epub 2021 Apr 29.
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 ( ), 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.
使用数字设备的互联网使用依赖速度之快也导致了巨大的数据流量。处理这些服务的传统方法是增加基础设施。然而，这会导致高昂的实施成本。因此，为了克服数据负担，研究人员提出了使用NP难目标集选择（TSS）问题解决方案的数据卸载方案。我们的工作重点是TSS优化和相应的数据卸载方案。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和一种机会主义数据卸载算法。所提出的方案具有阶为（ ）的总体多项式时间复杂度，其中k是收敛的主要目标集中节点的数量。然而，出于实际原因，我们已将其实现为线性阶。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，所提出的最终目标集选择（FTSS）算法在通过蜂窝塔的流量方面比贪婪方法性能优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%。
