18 research outputs found
Small-Scale Markets for Bilateral Resource Trading in the Sharing Economy
We consider a general small-scale market for agent-to-agent resource sharing,
in which each agent could either be a server (seller) or a client (buyer) in
each time period. In every time period, a server has a certain amount of
resources that any client could consume, and randomly gets matched with a
client. Our target is to maximize the resource utilization in such an
agent-to-agent market, where the agents are strategic. During each transaction,
the server gets money and the client gets resources. Hence, trade ratio
maximization implies efficiency maximization of our system. We model the
proposed market system through a Mean Field Game approach and prove the
existence of the Mean Field Equilibrium, which can achieve an almost 100% trade
ratio. Finally, we carry out a simulation study motivated by an agent-to-agent
computing market, and a case study on a proposed photovoltaic market, and show
the designed market benefits both individuals and the system as a whole
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
Distance-Based Opportunistic Mobile Data Offloading.
Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS
Contract design for traffic offloading and resource allocation in heterogeneous ultra-dense networks
In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this work, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations (MBSs) and small-cell base stations (SBSs). We explore a scenario where SBSs’ capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations