3 research outputs found
Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process
With the rapid increase in demand for mobile data, mobile network operators
are trying to expand wireless network capacity by deploying wireless local area
network (LAN) hotspots on which they can offload their mobile traffic. However,
these network-centric methods usually do not fulfill the interests of mobile
users (MUs). Taking into consideration many issues, MUs should be able to
decide whether to offload their traffic to a complementary wireless LAN. Our
previous work studied single-flow wireless LAN offloading from a MU's
perspective by considering delay-tolerance of traffic, monetary cost and energy
consumption. In this paper, we study the multi-flow mobile data offloading
problem from a MU's perspective in which a MU has multiple applications to
download data simultaneously from remote servers, and different applications'
data have different deadlines. We formulate the wireless LAN offloading problem
as a finite-horizon discrete-time Markov decision process (MDP) and establish
an optimal policy by a dynamic programming based algorithm. Since the time
complexity of the dynamic programming based offloading algorithm is still high,
we propose a low time complexity heuristic offloading algorithm with
performance sacrifice. Extensive simulations are conducted to validate our
proposed offloading algorithms