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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mobile networks and applications Ročník 26; číslo 3; s. 1270 - 1280
Hlavní autoři: Sharma, Prince, Shukla, Shailendra, Vasudeva, Amol
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2021
Springer Nature B.V
Témata:
Algorithms
Communications Engineering
Computation offloading
Computer Communication Networks
Electrical Engineering
Engineering
Greedy algorithms
Heuristic methods
IT in Business
Network latency
Networks
Optimization
Polynomials
Mobile data offloading
Opportunistic communications
Target set selection
Overlapping communities
Ad-hoc networks
Data forwarding
ISSN:1383-469X, 1572-8153, 1572-8153
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1383-469X
1572-8153
1572-8153
DOI:10.1007/s11036-021-01760-2