Network Coding for Backhaul Offloading in D2D Cooperative Fog Data Networks

• Published in: Wireless communications and mobile computing Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Quinton, Ben, Aboutorab, Neda
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Coding; Complexity; Cooperation; Graph representations; Graph theory; Greedy algorithms; Heuristic; Heuristic methods; Internet of Things; Network servers; Shortest-path problems; Wireless networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2018/1245720

Future distributed data networks are expected to be assisted by users cooperation and coding schemes. Given the explosive increase in the end-users’ demand for download of the content from the servers, in this paper, the implementation of instantly decodable network coding (IDNC) is considered in full-duplex device-to-device (D2D) cooperative fog data networks. In particular, this paper is concerned with designing efficient transmission schemes to offload traffic from the expensive backhaul of network servers by employing IDNC and users cooperation. The generalized framework where users send request for multiple packets and the transmissions are subject to erasure is considered. The optimal problem formulation is presented using the stochastic shortest path (SSP) technique over the IDNC graph with induced subgraphs. However, as the optimal solution suffers from the intractability of being NP-hard, it is not suitable for real-time communications. The complexity of the problem is addressed by presenting a greedy heuristic algorithm used over the proposed graph model. The paper shows that by implementing IDNC in a full-duplex cooperative D2D network model significant reduction in the number of downloads required from the servers can be achieved, which will result in offloading of the backhaul servers and thus saving valuable servers’ resources. It is also shown that the performance of the proposed heuristic algorithm is very close to the optimal solution with much lower computational complexity.