Cooperative Data Offload in Opportunistic Networks: From Mobile Devices to Infrastructure

TL;DR

This paper introduces a probabilistic framework and algorithms for cooperative data offloading in opportunistic networks, significantly improving data delivery probabilities despite the NP-hard nature of the problem.

Contribution

It presents a novel probabilistic framework and heuristic algorithms for cooperative offloading, addressing the NP-hard problem with low computational complexity and distributed implementation.

Findings

Probabilistic framework accurately estimates data delivery probability.

Cooperative offloading substantially enhances data delivery success.

Heuristic and distributed algorithms outperform existing methods.

Abstract

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. In contrast to existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to intermittently connected infrastructure within a given time constraint, which is referred to as the \textit{cooperative offloading} problem. Unfortunately, the estimation of data delivery probability over an opportunistic path is difficult and cooperative offloading is NP-hard. To this end, we first propose a probabilistic framework that provides the estimation of such probability. Based on the proposed probabilistic framework, we design a heuristic algorithm to solve cooperative offloading at a low computation cost. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces. Experimental results show that the probabilistic framework can accurately estimate the data delivery probability, cooperative offloading greatly improves the delivery probability, the heuristic algorithm approximates the optimum, and the performance of both the heuristic algorithm and distributed algorithm outperforms other approaches.