Game Theoretic Scheme Research Articles

Energy efficient federated learning in internet of vehicles: A game theoretic scheme

AbstractA promising alternative to conventional centralized machine learning that protects privacy is federated learning. Energy efficiency, meanwhile, appears as a major problem in federated learning for the Internet of Vehicles due to frequent model aggregations. To reduce the convergence time, existing research focuses on improving vehicular communication. However, it increases the computational and communication load on servers and disregards the true energy costs of vehicles. Thus, we propose an energy‐efficient federated learning scheme based on two‐stage game theories. Specifically, the payoff function first takes into account the energy cost as well as the contribution of each vehicle, which wins the fair payment based on the Shapley value via a cooperative game. Second, under the fairness constraint, energy‐efficient vehicles cooperatively form several coalitions for further federated worker selection through multiple rounds of the hedonic game. Additionally, we also develop a multiagent reinforcement learning algorithm that is leveraged for coalition formation to lower the energy cost in the game. Furthermore, we demonstrate that the game in our scheme is Nash stable. Extensive experimental results show that the scheme improves energy efficiency by around 68.8% in the best case compared to the most widely used method.

MobiTrust: Trust-Aware Pricing Scheme for Edge-Based Mobile Sensor-Cloud for Vehicular IoT

In this work, we propose MobiTrust, a game theoretic pricing scheme, to address the problem of ensuring trust in the edge-enabled mobile sensor-cloud (MSC) architecture for vehicular Internet-of-Things (IoT). In the edge-enabled MSC, a sensor-cloud service provider (SCSP) utilises multiple sensor-equipped vehicular IoT-based devices belonging to different owners and multiple edge devices deployed by different edge infrastructure providers (IPs) to provision Sensors-as-a-Service (Se-aaS). Since these entities are revenue-driven and they depend on each other for successful service provisioning, there is a need to establish trust among them to ensure the sustainability of MSC. Existing works on sensor-cloud address the issue of ensuring trust among selfish device-owners. However, these works do not consider the presence of IPs and assume that the IoT devices are stationary in nature, which renders them unsuitable for MSC. Therefore, in this work, we aim to address this drawback of the existing literature by proposing MobiTrust, which is comprised of two components. First, we propose a mobility-aware trust model designed based on the Transferable Belief Model, which utilizes different types of evidence collected by the devices to estimate their trust values. Second, we propose a trust-based pricing and resource allocation scheme based on the Stackelberg game that aims to maximise the profits and satisfaction of the various stakeholders. We analyze the proposed scheme theoretically and examine the existence of equilibrium in the proposed game. Results of extensive simulations show that MobiTrust increases profits for the stakeholders and QoS by 9% and 11%, respectively, compared to benchmarks.

A Game-Theoretic Scheme for Parked Vehicle-Assisted MEC Computation Offloading

By offloading computation tasks, multi-access edge computing (MEC) supports diverse services and reduces delay and energy consumption of mobile devices (MDs). However, limited resources of edge servers may be the bottleneck for task computing in high-density scenarios. To address this challenge, by leveraging the underutilized resources of parked vehicles to execute tasks, we propose a parked vehicle-assisted multi-access edge computing (PV-assisted MEC) architecture, which enables MEC servers to expand their capability flexibly. To achieve efficient offloading, we propose a PV-assisted MEC offloading scheme in a multi-MD environment. We design a game-based distributed algorithm to minimize the overhead of MDs and further reduce the burden on the MEC server. Simulation results show that compared with the common MEC system, our scheme can reduce the burden on the MEC server by 5% and the offloading overhead by 17%.

Mobile Ad Hoc Network Security Using Mean Field Game Theoretic Threshold-Based Scheme

Background: Mobile ad hoc networks (MANET’s) have recently attracted attention as it is independent of any pre-existing network infrastructure or centralized administration. Security in MANET’s thus becomes a major concern due to its open and dynamic architecture. Objective: We have introduced a dynamic mean field game theoretic approach to enable an individual node in MANETs to make strategic security defense decisions without centralized administration. Method: The nodes in MANET’s act as a router to forward data packets and exchange routing information. Ad-hoc On demand Distance Vector (AODV) protocol is one of the standard MANET routing protocols which can be easily attacked by the fraudulent nodes. The fraudulent nodes can be deceptive and mislead the transmission of data packets in the network by providing shorter path and highest destination sequence number. Game theory finds wide application as a statistical and mathematical tool to model such dynamic networks and provide security. Results: We have implemented mean field game theory for addressing security issue in MANET’s. Each node in this dynamic distributed network, knows the information about its own state as well as the average reflection of the whole mean field. The players can strategically make distributed security defense decisions under adverse conditions. Unlike static threshold-based scheme for security, in our study the threshold is estimated dynamically. Each node checks whether the received Route REPly (RREP) sequence number is higher than a dynamically updated threshold value. Conclusion: The comparative performance analysis of throughput (TR), packet delivery rate (PDR) and average cost (AC) has been demonstrated. Game theory has a vital role to validate and justify the intuitive strategic actions taken by each player to maximize their utility by playing optimal strategy. On the basis of the dynamic threshold calculated, the higher throughput and PDR could be achieved by eliminating the misleading paths. Simulation results corroborate that our dynamic mean field game theoretic scheme outperforms the static scheme. Discussion: A dynamic approach for mobile ad hoc networks is presented in this paper to improve the performance of the network in hostile environment. We have introduced a dynamic mean field game theoretic approach to enable an individual node in MANETs to make strategic security defense decisions without centralized administration. In this dynamic distributed network, each node in the proposed scheme only needs to know its own state information and the average reflection of the whole mean field.

Optimal Game-Based Energy Management with Renewable Energy for Secure Electric Vehicles Charging in Internet of Things

The integration of smart grid and Internet of Things (IoT) has been facilitated with the proliferation of electric vehicles (EVs). However, due to EVs’ random mobility and different interests of energy demand, there exists a significant challenge to optimally schedule energy supply in IoT. In this paper, we propose a secure game theoretic scheme for charging EVs supplied by mobile charging stations (MCSs) in IoT, considering the dynamic renewable energy source. Firstly, the charging system composed of MCSs is developed to implement the charging service. Secondly, when the secure charging scheme of EV users is designed, the utility function of each entity in the charging system is formulated to express the trading relationship between EV users and MCSs. Moreover, with consideration of the competition and cooperation, we propose a Stackelberg game framework with sub-noncooperative optimization. Thirdly, the existence and uniqueness of both Stackelberg equilibrium (SE) and Nash equilibrium (NE) are theoretically analyzed and proved. Through the presented distributed energy scheduling algorithm, we can achieve the optimal solution. Finally, numerical results demonstrate the effectiveness and efficiency of our proposal through comparison with other existing schemes.

Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing

Due to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more than one UAVs for the effective scheduling under multi-task requests. Based on this scenario, we clarify the issue on how to maintain the energy efficiency of UAVs and guarantee the reputation gain during the scheduling deployment. In this paper, we first formulate the energy consumption and reputation into the decision model of UAVs scheduling. A game-theoretic scheme is then developed for the optimal decision searching. With the developed model, a range of important parameters of UAV scheduling are thoroughly investigated. Our numerical results show that the proposed scheduling strategy is able to increase the reputation and decrease the energy consumption of UAVs simultaneously. In addition, in the game process, the profit of an operator can be maximized and the network economy research can be explored.

A Game Theoretic Scheme for Collaborative Vehicular Task Offloading in 5G HetNets

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes.

Energy–Latency Tradeoff for Computation Offloading in UAV-Assisted Multiaccess Edge Computing System

Unmanned aerial vehicles (UAVs) have been considered as a promising approach for providing additional computation capability and extensive coverage to ground mobile devices (MDs), especially in the scenario where the communication infrastructure is unavailable. This article investigates a UAV-assisted multiaccess edge computing system, where a number of ground MDs are served by a flying UAV and a ground base station, both of them are equipped with computation resources. The system aims to minimize the weighted cost of time latency and energy consumption, subject to the constraints on offloading decisions and resource competition. Since this problem is NP-hard, and the MDs are autonomous, we propose a game theory-based scheme to find the optimal solution and prove the existence of the Nash equilibrium. Meanwhile, we propose another two schemes as the benchmark to evaluate the efficiency and effectiveness of the game-theoretic solution. The simulation results show that the game-theoretic scheme is able to achieve a near-optimal performance, and the convergence time is also stable when the number of MDs increases.

Modeling an Attack-Mitigation Dynamic Game-Theoretic Scheme for Security Vulnerability Analysis in a Cyber-Physical Power System

The rapid development of advanced information and communication technology has made modern power systems evolve into more complicated cyber-physical power systems (CPPSs) with mutual coupling characteristics between cyber systems and power systems, and at the same time, the CPPSs have to confront some newly emerged risks owing to cyber system unreliability or cyberattacks. In this paper, regarding the cyber and physical attacks in a CPPS, the operation risks and vulnerabilities of transmission lines are discussed in detail by building relevant game-theoretic models. Under two possible cyberattack scenarios, namely time delay of system recovery and distributed denial of service, a three-stage defender-attacker-defender tri-level mathematical programming model is proposed based on dynamic game theory of complete information. In particular, the objective functions and corresponding constraint conditions in each level are analyzed and constructed elaborately. For the solution of this proposed tri-level programming model, a solution method based on an improved particle swarm optimization approach combined with sequential quadratic programming technique is applied during analysis. Finally, the proposed model is validated through two case studies, and some preliminary concluding remarks are summarized.

Decentralized Game-Theoretic Scheme for D-EVSE Based on Renewable Energy in Smart Cities: A Realistic Scenario

In this paper, we address a decentralized power production and management system based on Game Theory (GT) for Electric Vehicles' (EVs') interplay with a Decentralized Electric Vehicle Supply Equipment (D-EVSE) located at the public supply station. Renewable energy production such as solar energy (PV) is considered as the main power source for our D-EVSE and we consider the connection to the grid when the solar renewable energy system is failing to respond to the demand. We propose a decentralized GT (D-GT) scheme aiming to optimize the EVs' interaction with the D-EVSE considering both EVs' satisfaction as well as the D-EVSEs' stability. Also, the D-GT model is used to choose the optimal available solution for EV charging or discharging services that fulfill predefined constraints. A realistic scenario is considered as a testbed for our D-GT optimization model. Simulation results indicate that the proposed model can manage and control the interaction between EVs and D-EVSEs efficiently.

A Game Theoretic Scheme for Optimal Access Control in Heterogeneous Vehicular Networks

The heterogeneous vehicular networks (HetVNETs), which apply the heterogeneous access technologies ( e.g. , cellular networks and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and practical paradigm to enable vehicular service applications on the road. However, with different costs in terms of latency time and price, how to optimize the connection along the vehicle’s trip toward the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access networks, we first model the cost of each vehicle to download the requested content by jointly considering the vehicle’s requirements of the requested content and the features of the available access networks, including conventional vehicle to vehicle communication and the heterogeneous access technologies. A coalition formation game is then introduced to formulate the cooperation among vehicles based on their different interests (contents cached in vehicles) and requests (contents to be downloaded). After forming the coalitions, vehicles in the same coalition can download their requested contents cooperatively by selecting the optimal access network to achieve the minimum costs. The simulation results demonstrate that the proposed game approach can lead to the optimal strategy for the vehicle.

Tree-Based Attack–Defense Model for Risk Assessment in Multi-UAV Networks

Unmanned aerial vehicles (UAVs) networks are attack-prone networks due to high mobility and distributed nature. Most of the existing approaches focus on prevention instead of analyzing the security vulnerabilities. For an efficient analysis, the interaction between attackers and defenders need to be investigated. Hence, a tree-based attack–defense model for security analysis of multi-UAV networks has been discussed in this paper. An attack–defense tree has been designed that depicts every move of the defender with respect to the attacker's strategies. Using this attack–defense tree, a game theoretic scheme for risk assessment is formulated. The efficiency of the proposed scheme has been evaluated using a case study for the distributed denial-of-service attack.

Distributed algorithm to reduce contention in emergency situations by deploying cognitive radio ad‐hoc controllers

Nowadays, the main focus of emergency communication research is directed to the use of cognitive radio ad-hoc networks for disaster scenarios. This study addresses an efficient cooperative game-theoretic channel access scheme for data offloading and cooperation of emergency user using cooperative cognitive ad-hoc controllers in order to reduce the blocking probability of the newly incoming emergency user. The main objective is to connect all the users who are in need of urgent spectrum and it is considered that their data size is too large. Also, a hybrid overlay–underlay approach was undertaken which provides higher spectrum flexibility. A distributed algorithm is proposed which comprises a criterion assignment, merge and split algorithm and an auction mechanism to provide incentives to those controllers who participate in cooperation. An aggregate throughput of 18% is obtained as an improvement when compared with other conventional heuristics algorithm.

A Game Theoretic Interference Management Scheme in Full Duplex Cellular Systems under Infeasible QoS Requirements

Several emerging mobile applications and services (e.g., autonomous cars) require higher wireless throughput than ever before. This demand stresses the need for investigating novel methods that have the potential to dramatically increase the spectral efficiency (SE) of wireless systems. An evolving approach is the Single-channel full duplex (SCFD) communication where each node may simultaneously receive and transmit over the same frequency channel, and, hence, this could potentially double the current SE figures. In an earlier research work, we derived a model of the signal to interference plus noise ratio (SINR) in an SCFD-based cellular system with imperfect self interference cancellation, and investigated interference management under feasible QoS requirements. In this paper, game theoretic results are exploited to investigate the intercell interference management in SCFD-based cellular networks under infeasible QoS requirements. The investigation starts with a game formulation that captures two different cases. Then, the existence and uniqueness of the Nash equilibrium point are established. After that, a computationally efficient distributed algorithm, which realizes best effort and fair wireless services, is designed. The merit of this scheme is that, when the QoS requirements are feasible, they will be achieved with minimum energy consumption. Results of extensive simulation experiments are presented to show the effectiveness of the proposed schemes.

Fair and Efficient Rate Allocation for Wireless-Powered Machine-Type Communication Networks

This paper proposes a bargaining game theoretic rate allocation scheme for wireless-powered machine-type communications (MTCs). In the considered body area MTC network (MTCN), a battery-powered user equipment (UE) acting as the MTC gateway (MTCG) is responsible for collecting the information uploaded by in/on body wireless-powered MTC devices (MTCDs). By solving the Nash bargaining solution (NBS) of the proposed cooperative game, the minimum rate requirements of the MTCDs are satisfied. In addition, the network resource can be allocated to the MTCDs in a fair and efficient manner regarding the difference of their channel qualities. In comparison to other traditional resource allocation methods, the simulation results show that the proposed NBS-based method obtains a good tradeoff between the system efficiency and per-node fairness.

Energy Efficiency Analysis by Game-Theoretic Approach in the Next Generation Network

This paper presents a game-theoretic scheme with anti-coordinated players by incorporating adaptation of femto base station (FBS) transmit power, attenuation of interference and utility function for open access mode and closed access mode, respectively. The deployment of femtocells in the networks is to produce improved energy efficiency (EE) and optimized response of payoff function. Additionally, the operating principle of the spectrum sharing scheme has been discussed in which FBS as a player acquire knowledge from utility responses of their strategic communications and revise their strategies at each level of the game process. Here, an FBS is regarded as a player in the game to select those users who are satisfied to a greatest extent and besides an FBS plays a role of mentor. Thereafter, the equilibrium concept has been invoked to aid the anti-coordinated players for the strategies. Finally, validated the simulation results are with its rarely studied extension in cognitive-femtocell networks.

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.

Light-Weight Stackelberg Game Theoretic Demand Response Scheme for Massive Smart Manufacturing Systems

As Internet-of-Things (IoT) technology emerges, smart manufacturing has recently attracted a large amount of attention. Smart manufacturing leads to smart energy management because of its significant operating expenditure savings. However, it is believed that centralized energy management of IoT devices will impose critically large overhead since massive numbers of IoT devices are expected to be deployed. Therefore, distributed energy management or demand response (DR) is deemed to be a better solution for emerging massive smart manufacturing systems. There have been a significant number of distributed DR algorithms, including Stackelberg game theoretic approaches. However, the Stackelberg game theoretic approaches require a large number of iterations to reach Nash equilibrium, which in turn necessitates communication overheads among IoT devices. This communication overhead causes a large amount of energy consumption as well as delay. In this paper, we propose a light-weight DR scheme based on the Stackelberg model without iterations for the massive smart manufacturing systems. The proposed scheme manages energy consumption based on a non-iterative Stackelberg model and historical real-time pricing. To the best of our knowledge, our approach is the first technique that considers communication overheads for the DR technique. The performance evaluation demonstrates that the proposed scheme shifts operations to avoid peak loads, and the electricity bill is significantly reduced, operations occur at preferred times, and communication energy consumption and delay are minimized.

Game-theoretic privacy preserving constructions for rational and malicious secret sharing models for collaborative frequent itemset mining

Collaborative data mining amongst parties necessitates resolving the concern of maintaining the privacy of their data. Shamir's secret sharing, an information theoretically secure scheme, has been proposed for different scenarios in privacy preserving distributed frequent itemset mining (PPDFIM). However, one of the goals of PPDFIM is to ensure maximum participation from the contending participants. Hence, the scheme proposed must incorporate preventive and corrective measures, aimed at correcting the negatively performing participants. We resort to a game-theoretic approach for the purpose. We extend our proposed verifiable secret sharing (VSS)-based scheme for PPDFIM with a novel game-theoretic scheme called 'ratings-based game-theoretic' (RGT) scheme. In our improved scheme for PPDFIM, the parties with negative intent improve their behaviour tending the setup towards the coveted nash equilibrium (NE). We compare our proposed non-game-theoretic VSS-based scheme and the proposed RGT scheme theoretically and empirically in terms of utility of output and reduction in execution cost.

Game-theoretic Privacy Preserving Constructions for Rational and Malicious Secret Sharing Models for Collaborative Frequent Itemset Mining

Collaborative data mining amongst parties necessitates resolving the concern of maintaining the privacy of their data. Shamir's secret sharing, an information theoretically secure scheme, has been proposed for different scenarios in privacy preserving distributed frequent itemset mining (PPDFIM). However, one of the goals of PPDFIM is to ensure maximum participation from the contending participants. Hence, the scheme proposed must incorporate preventive and corrective measures, aimed at correcting the negatively performing participants. We resort to a game-theoretic approach for the purpose. We extend our proposed verifiable secret sharing (VSS)-based scheme for PPDFIM with a novel game-theoretic scheme called 'ratings-based game-theoretic' (RGT) scheme. In our improved scheme for PPDFIM, the parties with negative intent improve their behaviour tending the setup towards the coveted nash equilibrium (NE). We compare our proposed non-game-theoretic VSS-based scheme and the proposed RGT scheme theoretically and empirically in terms of utility of output and reduction in execution cost.