Maximizing Profit in Green Cellular Networks through Collaborative Games

In this paper, we deal with the problem of maximizing the profit of Network Operators (NOs) of green cellular networks in situations where Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations (BSs) can be shared among different operators. We show that if NOs cooperate among them, by mutually sharing their users and BSs, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among NOs. Furthermore, we propose a mathematical optimization model to allocate users to a set of BSs, in order to reduce costs and, at the same time, to meet user QoS for NOs inside the same coalition. Based on this, we propose an algorithm, based on cooperative game theory, that enables each operator to decide with whom to cooperate in order to maximize its profit. This algorithms adopts a distributed approach in which each NO autonomously makes its own decisions, and where the best solution arises without the need to synchronize them or to resort to a trusted third party. The effectiveness of the proposed algorithm is demonstrated through a thorough experimental evaluation considering real-world traffic traces, and a set of realistic scenarios. The results we obtain indicate that our algorithm allows a population of NOs to significantly improve their profits thanks to the combination of energy reduction and satisfaction of QoS requirements.Comment: Added publisher info and citation notic

CLEVER: a cooperative and cross-layer approach to video streaming in HetNets

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (eNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of eNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer aswell as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE)

Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.Peer ReviewedPostprint (author's final draft

Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios