Game Theoretic Resource Allocation Research Articles

Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing

Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing

QoS-Aware Resource Allocation for Green Sensor-as-a-Service Provisioning in Vehicular Multi-Sensor-Cloud

This work addresses the problem of optimal resource allocation for provisioning green Sensor-as-a-Service (Se-aaS) in the presence of multiple sensor-cloud service providers (SCSPs) and vehicle owners (VOs) having heterogeneous sensors. To ensure high service availability, low carbon footprint, and optimal profit distribution, optimal usage of the available resources while maintaining optimal contribution of VOs in the multi-sensor-cloud (MSC) market is necessary. Additionally, a single SCSP may lose revenue due to the absence of compatible sensor-equipped vehicles (SVs) to serve user requests. To address these issues, we propose FRAME, a game-theoretic resource allocation scheme, for vehicular MSC. Initially, we use a single-leader-multiple-followers Stackelberg game to decide the optimal set of SVs for serving a request while ensuring high QoS, low energy consumption, and optimal profit distribution among the SCSPs and VOs. Further, to address the inadequacy of resources, we propose a collaboration scheme using the expected utility theory for multiple SCSPs and choose the optimal SCSP to serve the requests. We evaluate the performance of FRAME theoretically and experimentally and compare it with benchmark schemes. Simulation results depict a 4.14-27.3% increase in served requests, a 14.61-22.83% decrease in energy consumption, and optimal profit distribution among the SCSP and VOs.

Evolutionary game theoretic resource allocation simulation for molecular communication

Molecular Communication (MC) is an emerging technology using molecules to transfer information between nanomachines. In this paper, we approach the resource allocation problem in Molecular Nano-networks (MCN) from the perspective of evolutionary game theory. In particular, we consider an MCN as an organism having three types of nodes acting as a sensor, relay, and sink, respectively. The resources are distributed among the nodes according to an evolutionary process, which relies on the selection of the most successful organisms followed by creating their offspring iteratively. In this regard, the success of an organism is measured by the total number of dropped messages during its life cycle. To illustrate the evolution procedure, we design a toy problem, and then solve it analytically and using the evolution approach for comparison. We further simulate the performance of the evolution approach on randomly generated organisms. The results reveal the potential of evolutionary game theory tools to improve the transmission performance of MCNs.

Game theoretic and non-game theoretic resource allocation approaches for D2D communication

Device to Device communication involves communiqué between two devices without routing the packet through the cellular network. Ideally, the process involves reutilizations of the ideal resources of the cellular network for communication purpose. This ensures better management of radio spectrum and reduces communication delay. However, resource allocation has also been a major challenge because it contributes to various problems like interference, throughput issues and many other. A lot of work has been carried out in the recent past to address the above issue. The current paper reviews the work done in the area of resource allocation in the context of device to device communication. Among various mathematical tools available it was bring into being that game theory has been extensively used to address the problem. The main contribution of the paper, it provides the insight into the evolvement that has been made in the area of resource allocation and highlight various open issues that needs to be addressed.

Game-Theoretic Resource Allocation Algorithms for Device-to-Device Communications in Fifth Generation Cellular Networks: A Review

Game-Theoretic Resource Allocation Algorithms for Device-to-Device Communications in Fifth Generation Cellular Networks: A Review

An Attacker-defender Resource Allocation Game with Substitution and Complementary Effects.

The United States is funding homeland security programs with a large budget (e.g., 74.4 billion for FY 2019). A number of game-theoretic defender-attacker models have been developed to study the optimal defense resource allocation strategies for the government (defender) against the strategic adversary (attacker). However, to the best of our knowledge, the substitution or complementary effects between different types of defensive resources (e.g., human resource, land resource, and capital resource) have not been taken into consideration even though they exist in practice. The article fills this gap by studying a sequential game-theoretical resource allocation model and then exploring how the joint effectiveness of multiple security investments influences the defensive budget allocation among multiple potential targets. Three false belief models have been developed in which only the defender, only the attacker, and both the defender and attacker hold false beliefs about the joint effectiveness of resources. Regression analysis shows that there are significant substitution effects between human and capital resources. The results show that the defender will suffer a higher loss if he fails to consider the substitution or complementary effects. Interestingly, if the attacker holds a false belief while the defender does not, the defender will suffer an even higher loss, especially when the resources are substitutes. However, if both the attacker and defender hold false beliefs, there will be lower loss when resources are complementary. The results also show that the defender should allocate the highly effective resource when the resources substitute each other. This article provides some new insights to the homeland security resourceallocation.

Game theoretic resource allocation model for designing effective traffic safety solution against drunk driving

To reduce the number of deaths and injuries due to drunk driving (also referred to as drink driving, driving while intoxicated, and driving under the influence of alcohol in the literature), many countries have deployed public safety resources to inspect traffic network. However, challenges remain in allocating limited public safety resources to the significantly large traffic networks. In this paper, we propose an optimal public safety resource allocation scheme to inspect drunk driving. To highlight the utilization of limited public safety resources, first, we model the issue of drunk driving as a defender-attacker Stackelberg game. In the game, the law enforcement agency (the defender) allocates public safety resources in a traffic network to arrest drunk drivers (the attackers), and the attacker seeks to choose a feasible route given the defender's strategy to maximize the escape probability. Second, we develop an effective approach to compute the optimal defender strategy based on a double oracle framework. Third, we analyze the complexity of the defender oracle problem. Then, we conduct simulations on directed graphs, which are abstracted from the city traffic network in Dalian, China, to demonstrate that our scheme achieves a robust solution and higher utility, and is capable of scaling up to handle realistic-sized drunk-driving problems.

Game theoretic efficient radio resource allocation in 5G resilient networks: A data driven approach

AbstractIn recent years, 5G resilient networks have gained significant attention in the wireless industry. The prime concern of commercial networks is to maximize network capacity to increase their revenue. However, in disaster situations during outages when cell sites are down, instead of capacity, coverage becomes predominant. In this paper, we propose a game theory–based optimal resource allocation scheme, while aiming to maximize the sum rate and coverage probability for the uplink transmissions in disaster situations. The proposed hierarchical game theoretical framework optimizes the uplink performance in multitier heterogeneous network with pico base stations and femto access points overlaid under a macro base station. The test simulations are based on a real‐time data set obtained for a predefined amount of time. The data statistics are then manipulated to create practical disaster situations. The solution for the noncooperative game has been obtained by using pure strategy Nash equilibrium. We perform simulations with different failure rates and the results show that the proposed scheme improves the sum rate and outage probability by significant margin with or without disaster scenario.

A Novel Game Theoretic Method for Efficient Downlink Resource Allocation in Dual Band 5G Heterogeneous Network

Hybrid heterogeneous wireless networks utilizing both traditional microwave frequency band and millimetre wave band are currently been investigated as a potential approach to meet the increasing demand for ultra-high rate transmission with the severe microwave spectrum scarcity and requirement for low power network devices. In this paper, we investigate downlink resource allocation in two-tier heterogeneous networks comprising of a macrocell transmitting at a microwave frequency and dual-band small cells utilizing both microwave and millimetre wave frequencies. We present a novel architecture with dual band small cell base stations. The small cell coverage area is divided into two regions where the users in the inner and outer regions are served by the associated small cells on millimetre wave and microwave frequencies, respectively. We formulate a two layer game theory based approach for maximizing energy efficiency and spectral efficiency of the system with optimal usage of available radio resources. The proposed game theoretic approach comprises of a non-cooperative frequency assignment game as its first layer and a multi-objective optimization based game as the second layer. In the frequency assignment game, each small cell base station selects a frequency band from either the microwave band or millimetre wave band for each of its associated users by maximizing the data rate of its users. The solution to the frequency assignment game is obtained via Pure Strategy Nash Equilibrium. The utility function of the game in the second layer involves power and sub-carrier allocation via the joint maximization of both energy efficiency and spectral efficiency of the network. The utility function is formulated as a multi-objective optimization problem which is converted into a single objective problem and solved using Lagrangian dual relaxation. Simulations results show that the proposed dual band heterogeneous network with game theoretic resource allocation offers improved sum rate, energy efficiency and spectral efficiency compared to classical shared spectrum heterogeneous network utilizing only microwave frequency band.

Game‐theoretic radio resource management for relay‐assisted access in wireless networks

The radio resource management for relay-assisted access where every terminal user communicates with the base station through relay users is studied. In particular, the problem formulation is to maximise the total utility across relay users and terminal users under the bandwidth constraint of device-to-device (D2D) links. To solve such an optimisation problem, the authors first explore the diversity of channels between the relay users and the terminal users based on the maximum weighted bipartite matching theory, and adopt Hungarian algorithm to select the best relay user for each terminal user. Then, to stimulate relay users to participate in the cooperation, they design a two-stage Stackelberg game to jointly maximise the utilities of the selected relay user and the terminal user. In this doing, every terminal user can obtain the optimal data rate with the aid of relay. Finally, the authors' simulations show that the proposed relay user selection and game-theoretic resource allocation scheme can effectively improve the downloading rates of terminal users and achieve a ‘win-win’ strategy between the terminal users and relay users for relay-assisted access using D2D communications.

A Game-Theoretic Resource Allocation Approach for Intercell Device-to-Device Communications in Cellular Networks

Device-to-device (D2D) communication is a recently emerged disruptive technology for enhancing the performance of current cellular systems. To successfully implement D2D communications underlaying cellular networks, resource allocation to D2D links is a critical issue, which is far from trivial due to the mutual interference between D2D users and cellular users. Most of the existing resource allocation research for D2D communications has primarily focused on the intracell scenario while leaving the intercell settings not considered. In this paper, we investigate the resource allocation issue for intercell scenarios where a D2D link is located in the overlapping area of two neighboring cells. Furthermore, we present three intercell D2D scenarios regarding the resource allocation problem. To address the problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely, base stations, as players competing resource allocation quota from D2D demand, and we define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the Nash equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance, including sum rate and sum rate gain, but also shed lights on resource configurations for intercell D2D scenarios.

Game Theoretic Resource Allocation in Media Cloud With Mobile Social Users

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably.

Game-theoretic resource allocation for full-duplex communications

The recent significant progress in realizing FD systems has opened up a promising avenue for increasing the capacity of future wireless networks. In addition to hardware design and signal processing techniques, radio resource management is essential in the design and implementation of practical FD communication networks. FD communications introduce additional dimensions of network resources requiring the development of new resource allocation algorithms. In this article we concentrate on distributed radio resource allocation, and show how game-theoretic models can be used to effectively solve the optimization problems in FD communication systems. Finally, we outline several key open research directions in this emerging area.

Resource Allocation With Partially Overlapping Filtered Multitone in Cognitive Heterogeneous Networks

Partially overlapping tones have recently been offered as a new solution to reduce the other user interference in cognitive heterogeneous networks. By introducing intentional carrier frequency shift, the users can exploit the gaps between the subcarriers. In this letter, we perform game theoretical resource allocation with partially overlapping filtered multitone signaling. To achieve the resource allocation, a secondary base station slides a group of consecutive subcarriers through all available ones and computes the utility for each selected subcarriers. It picks the consecutive ones, which give the highest capacity result. By performing this scheme, the other user interference is reduced significantly with a slightly slower convergence rate.

One Size Does Not Fit All: A Game-Theoretic Approach for Dynamically and Effectively Screening for Threats

An effective way of preventing attacks in secure areas is to screen for threats (people, objects) before entry, e.g., screening of airport passengers. However, screening every entity at the same level may be both ineffective and undesirable. The challenge then is to find a dynamic approach for randomized screening, allowing for more effective use of limited screening resources, leading to improved security. We address this challenge with the following contributions: (1) a threat screening game (TSG) model for general screening domains; (2) an NP-hardness proof for computing the optimal strategy of TSGs; (3) a scheme for decomposing TSGs into subgames to improve scalability; (4) a novel algorithm that exploits a compact game representation to efficiently solve TSGs, providing the optimal solution under certain conditions; and (5) an empirical comparison of our proposed algorithm against the current state-of-the-art optimal approach for large-scale game-theoretic resource allocation problems.

GALLERY: A Game-Theoretic Resource Allocation Scheme for Multicell Device-to-Device Communications Underlaying Cellular Networks

Device-to-device (D2D) communication has recently emerged as a promising technology to improve the capacity and coverage of cellular systems. Coordinating interference between D2D and cellular users plays a crucial role in realizing D2D communications underlaying cellular networks successfully. While most of prior mechanisms for D2D have focused on mitigating interference within a single-cell system, they fail to address intercell interference with multiple cell settings. In this paper, we investigate the intercell interference issue in a cellular network where a D2D link reuses the available spectrum resources of multiple cells. We propose a game-theoretic resource allocation scheme, termed GALLERY, to address this problem. Unlike existing works that typically treat D2D users as players, we characterize base stations (BSs) as players competing for resources allocation quota of D2D demand, and define the utility of each player as the payoff gained from both cellular and D2D. We also propose a resource allocation protocol based on the equilibrium derivation. Extensive simulations are conducted to verify the proposed scheme and the results show that it can considerably enhance the system performance in terms of sum rate and sum rate gain. It is expected that GALLERY provides systematical insights into resource configurations of multiple cells for D2D communications.

Game Theoretic Resource Allocation for D2D MIMO Heterogenous Networks

In this paper, we formulate a sum-rate maximization problem for device-to-device (D2D) communications underlaying downlink multiple-input multiple-output heterogenous cellular networks subject to the quality of service constraints and the interference temperature constraint. Since it is difficult to obtain the optimal solution due to the high complexity, game theory is used to study the resource allocation in such networks. The proposed game is a potential game where the existence of Nash equilibrium (NE) is guaranteed. A practical resource allocation algorithm based on better response dynamic is proposed. We prove that the proposed algorithm can converge to a feasible NE. Numerical results show that the proposed algorithm can achieve near optimal sum rate performance with low complexity.

Game-theoretic resource allocation and decoding order control in OFDMA based multihop networks

It is well known that the information theoretic multiple access channel capacity region is known to be achieved by successive decoding schemes. In this paper, we study a distributed multiple access scheme in OFDMA-based wireless multihop networks. As a solution, we propose a game-theoretic resource allocation and decoding order control scheme, which is based on iterative multi-unit second price auction. The key concept of the proposed scheme is to update the auction value based on the difference between current and target bidding efficiency. We prove our proposal's convergence and stability through analysis and show its performance using simulation. It achieves around 80% of the full-search-based sum throughput while providing inter-user and inter-subchannel fairness.

Efficient Mobile Cloud Service Allocation For Mobile Commerce

This paper presents efficient mobile cloud service allocation for mobile commerce in mobile cloud. Mobile cloud service allocation scheme considers both mobile clients and cloud datacentre providers. The mobile commerce service scenario is taken as an example in mobile cloud environment, and our service allocation approach is applied to mobile cloud environment. The paper proposes a distributed mobile cloud service allocation algorithm for mobile commerce (SAAMC). From the simulation results, the execution success ratio of our SAAMC is better than that of game-theoretic mobile cloud resource allocation (GMCRA) (Ge et al., 2012) and auction-based resource allocation algorithm (ARAA) (Zhang et al., 2013). The allocation efficiency of SAAMC is better than that of GMCRA and ARAA. The energy consumption ratio of SAAMC is not good as that of GMCRA.

Game-Theoretic Resource Allocation for Protecting Large Public Events

High profile large scale public events are attractive targets for terrorist attacks. The recent Boston Marathon bombings on April 15, 2013 have further emphasized the importance of protecting public events. The security challenge is exacerbated by the dynamic nature of such events: e.g., the impact of an attack at different locations changes over time as the Boston marathon participants and spectators move along the race track. In addition, the defender can relocate security resources among potential attack targets at any time and the attacker may act at any time during the event. This paper focuses on developing efficient patrolling algorithms for such dynamic domains with continuous strategy spaces for both the defender and the attacker. We aim at computing optimal pure defender strategies, since an attacker does not have an opportunity to learn and respond to mixed strategies due to the relative infrequency of such events. We propose SCOUT-A, which makes assumptions on relocation cost, exploits payoff representation and computes optimal solutions efficiently. We also propose SCOUT-C to compute the exact optimal defender strategy for general cases despite the continuous strategy spaces. SCOUT-C computes the optimal defender strategy by constructing an equivalent game with discrete defender strategy space, then solving the constructed game. Experimental results show that both SCOUT-A and SCOUT-C significantly outperform other existing strategies.