Double Auction Offloading for Energy and Cost Efficient Wireless Networks

Abstract

Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically with changing roles leading to a novel market model, where innovative challenges are introduced. In this paper, a novel resource sharing and offloading algorithm is introduced based on a double auction mechanism where MNOs and third parties buy and sell capacity and roam their traffic among each other. For low traffic periods, Base Stations (BSs) and Small Cells (SCs) can even be switched off in order to gain even more in energy and cost. Due to the complexity of the scenario, we adopt the multi-objective optimization theory to capture the conflicting interests of the participating entities and we design an iterative double auction algorithm that ensures the efficient operation of the market. Additionally, the selection of the appropriate time periods to apply the proposed algorithm is of great importance. Thus, we propose a machine learning technique for traffic load prediction and for the selection of the most effective time periods to offload traffic and switch off the Base Stations. Analytical and experimental results are presented to assess the performance of the algorithm.

An Auction Pricing Model for Energy Trading in Electric Vehicle Networks

“…Thus, the CSs acknowledge to the auctioneer their asks. In a previous work [39], we exploited a double auction approach to mathematically formulate a different problem. A resource-sharing market was developed in order to motivate mobile network operators (owning base stations, with various traffic load and diverse numbers of subscribers) and a third party (owning a network of small cells) to trade bandwidth resources through a double auction mechanism.…”

“…However, there are different solutions that represent the best feasible state of the investigated system. In our previous work [39], the social welfare multi-objective maximization problem is solved through the use of the method of Lagrange multipliers and the determination of the Karush-Kuhn-Tucker conditions. In [39], the energy market consisted of one third-party small cell network owner and just a few (up to five) mobile network operators.…”

“…In our previous work [39], the social welfare multi-objective maximization problem is solved through the use of the method of Lagrange multipliers and the determination of the Karush-Kuhn-Tucker conditions. In [39], the energy market consisted of one third-party small cell network owner and just a few (up to five) mobile network operators. However, in the energy market that we investigate, we came to the conclusion that this method was not suitable.…”