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Vehicular Content Centric Network (VCCN) has been proposed to address the issues of user mobility and sporadic connectivity in VANET. The in-network caching of VCCN can indeed improve the performance of content distribution. However, data caching unfortunately suffers from Cache Pollution Attack (CPA) that sends out fabricated requests to pollute the data cache. To prevent the degradation of network performance caused by such an attack, we propose a detection and defense scheme of CPA by adopting game theory. We model the poisoning attack scenario into a non-cooperative game model. First, we prove the non-existence of pure strategy Nash Equilibrium (NE) in the attacker and defender game and propose a normal form game model with complete information and an extensive Bayesian form game model with incomplete information. Under the guidance of NE, we design a punishment strategy to prevent the coordinated attack and make the attackers behave normally. Moreover, we design a cooperative detection scheme to give the road side unit the final decision on CPA based on the direct and indirect suspicious lists which are generated by each node according to the observed traffic pattern. Simulation evaluations demonstrate that our scheme has a better performance compared to other state-of-the-art schemes.

with the increase of mobile device usage around the globe, face-to-face communication has degraded and complete prohibition is not realistic. The degradation is not to be prevented as it happens as a side effect of technology. However, balancing usage of mobile device and quality of face-to-face communication is possible. Thus, by approaching users through developing a party genre mobile game, which is not famous as a mobile game at the time, with local multiplayer focus and introducing the given game to users, it is expected that face-to-face communication can be encouraged, thus resulting in mobile party game to become a media for the encouragement. Sample mini game that is a part of party game is developed with the help of Unity game engine as a testing object in which user response is collected. As application testing take place, and target user response is collected, face-toface encouragement is proven and user response is positive towards the game. Sample game testing has effectively reintroduced the existence of nowadays rare party game in mobile platform along with encouraging face-to-face communication, shown by the enthusiasm and enjoyment that audience testers have experienced. In the end, with mobile party game can help by being a media to encourage face-to-face communication and prevent degradation of such communication, future developments of a complete party game in mobile platform is highly encouraged as overall face-to-face communication quality enhancement may be required in the future in order to mitigate degradation.

Demand response (DR), an integral part of the smart grid, has great potential in handling the challenges of the existing power grid. The potential of different DR programs in the energy management of residential consumers (RCs) and the integration of distributed energy resources (DERs) is an important research topic. A novel distributed approach for energy management of RCs considering the competitive interactions among them is presented in this paper. The impact of participation of RC’s in price-based (PB) and incentive-based (IB) DR programs is investigated using game theory. For this, an energy management optimization problem (EMOP) is formulated to minimize electricity cost. The utility company employs electricity price as a linear function of aggregated load in the PB DR program and an incentive rate in the IBDR program. RCs are categorized into active and passive users. Active users are further distinguished based on the ownership of energy storage devices (SD) and dispatchable generation units (DGU). EMOP is modeled using a non-cooperative game, and the distributed proximal decomposition method is used to obtain the Nash equilibrium of the game. The results of the proposed approach are analyzed using different case studies. The performance of the proposed approach is evaluated in terms of aggregated cost and system load profile. It has been observed that participation in PB and IBDR program benefits both the utility and the consumers.

Energy saving income distribution mode is of great significance to the energy industry. With the continuous application of new technologies, the problem of excess energy saving income distribution has become one of the obstacles to the appreciation of energy performance. At present, the distribution of risk and income is mainly based on the contribution of risk and income, which has some limitations. The benefit distribution of energy saving negotiation between energy saving service companies and clients can be regarded as a bargaining process where an effective range satisfying both parties can be obtained. This provides a new perspective in solving the problem of excess energy saving income distribution in energy management contract projects.

With the vigorous development of Internet of Things technology, the current distribution network is developing towards the information-based and intelligent distribution Internet of Things (D-IoT). D-IoT adopts the mode of the cloud computing center and the edge cloud network working together. The edge cloud network has a large number of intelligent terminals, which can well adapt to the current sharply expanding power data scale. In order to further improve the ability of the edge network in D-IoT to process data in real time, and to maximize the quality of user experience (QoE) while minimizing energy consumption when performing computing offload, this paper proposes a dynamic non-cooperative game based edge Computing task offloading strategy, considering the dynamic nature of task generation, designed a distributed iterative optimization algorithm, which decomposes computing offloading into a series of sub-problems to solve. The results of simulation experiments prove that the calculation offloading mechanism proposed in this paper can greatly improve D -Compute efficiency of IoT system.

In this paper, the stability analysis of networked control systems (NCSs) under Denial-of-Service (DoS) attacks is studied. Firstly, the stability analysis for NCSs free of DoS attacks is investiga...

With the rapid development of intelligent hardware and the Internet of Things, the bandwidth and resource utilization efficiency of related networks need to be improved. At the same time, the corresponding communication spectrum is becoming more and more scarce. At present, cognitive D2D communication technology in 5G network can establish communication link directly through communication equipment, so that two established communication equipment can realize direct communication, so as to make full use of communication resources and finally meet the explosive growth of traffic demand. How to manage communication resources and select communication links is the main research direction of D2D communication technology in 5G technology. Based on the knowledge of game theory, this paper proposes a communication channel allocation scheme and resource optimization scheme based on spectrum clustering and non cooperative game to maximize the operation efficiency of D2D communication equipment. The actual efficiency can reach about 1.5 times of the original communication efficiency. Finally, the optimization method proposed in this paper is compared with the traditional communication method. The experimental results show that this method has higher advantages.

Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when CP negotiates with the ISP on behalf of the users to remove the network subscription fees so as to attract more users and increase the number of advertisements. As such, a key challenge is how to provide proper sponsorship in the situation of complex interactions among the telecommunication actors, namely, the advertisers, the content provider, and users. To answer those questions, we explore the potential economic impacts of this new pricing model by modeling the interplay among the advertiser, users, and the CPs in a game theoretic framework. The CP may have either a subscription revenue model (charging end-users) or an advertisement revenue model (charging advertisers). In this work, we design and analyze the interaction among CPs having an advertisement revenue as a non-cooperative game, where each CP determines the proportion of data to sponsor and a level of credibility of content. In turn, the end-users demand for the content of a CP depends not only on their strategies but also upon those proposed by all of its competitors. Through rigorous mathematical analysis, we prove the existence and uniqueness of the Nash equilibrium. Based on the analysis of the game properties, we propose an iterative algorithm, which guarantees to converge to the Nash equilibrium point in a distributed manner. Numerical investigation shows the convergence of a proposed algorithm to the Nash equilibrium point and corroborates the fact that sponsoring content may improve the CPs outcome.

I formalize the role of the self-signaling condition in guaranteeing coordination for pre-play cheap talk games with one-sided communication. I model a preexisting common language by assuming that the Receiver either ignores or follows cheap talk recommendations, but never inverts them. This assumption creates asymmetry between messages, which captures the essence of a common language. It does not rule out any outcome at hand in that every equilibrium outcome of the original game remains an equilibrium outcome in this transformed game. However, applying iterative admissibility to the transformed game yields sharp predictions. If the stage game satisfies a certain self-signaling condition, then the Sender gets her Stackelberg payoff in every iteratively admissible outcome. On the other hand, if the stage game violates a weaker self-signaling condition, miscoordination can happen in an iteratively admissible outcome.

As a key step in Six-Degree-of-Freedom (6DoF) point cloud registration, 3D keypoint technique aims to extract matches or inliers from random correspondences between the two keypoint sets. The major challenge in 3D keypoint techniques is the high ratio of mismatched or outliers in random correspondences in real-world point cloud registration. In this paper, we present a novel inlier extraction method, which is based on Supervoxel Guidance and Game Theory optimization (SGGT), to extract reliable inliers and apply for point cloud registration. Specifically, to reduce the scale of keypoint correspondences, we first construct powerful groups of keypoint correspondences by introducing supervoxels, which involves 3D spatial homogeneity. Second, to select promising combined groups, we present a novel ‘fit-and-remove’ strategy by incorporating 3D local transformation constraints. Third, to extract purer inliers for point cloud registration, we propose a grouping non-cooperative game algorithm, which considers the relationship between the combined groups. The proposed SGGT, by eliminating the mismatched combined groups globally, avoids the false combined groups that lead to the failed estimation of rigid transformations. Experimental results show that when processing on large keypoint sets, the proposed SGGT is over 100 times more efficient compared to the stat-of-the-art, while keeping the similar accuracy.

We consider land rental between a single tenant and several lessors. The tenant should negotiate sequentially with each lessor for the available land. In each stage, we apply the Nash bargaining solution, as a short-cut to solving non-cooperative bargaining games. Our results imply that, when all land is necessary, a uniform price per unit is more favorable for the tenant than a lessor-dependent price. Furthermore, a lessor is better off with a lessor-dependent price only when negotiating first. For the tenant, lessors’ merging is relevant with lessor-dependent price but not with uniform price. Ministry of Education of Peru | Ref. PRONABEC Ministerio de Economía y Competitividad | Ref. ECO2014-52616-R Agencia Estatal de Investigación | Ref. ECO2017-82241-R Xunta de Galicia | Ref. ED431B 2019/34

As a powerful tool of Demand Response (DR) techniques in smart grid market, Real-time Pricing (RTP) may optimize the electricity consumption pattern of users and improve the efficiency of electricity market. In this paper, a multi-leader-follower Stackelberg Game (SG) based on RTP is established to model the strategic interaction behavior between multiple electricity retailers and multiple users while simultaneously considering the power load uncertainty of users and the price competition among electricity retailers. In the game model, electricity retailers aim to seek their revenue maximization while the optimal power consumption competition among the users is taken into account. Lagrange multiplier method is utilized to solve the Nash Equilibriums (NE) of two non-cooperative games, and the closed-form optimal solution is obtained, then the Stackelberg Equilibrium (SE) consisting of the optimal real-time prices of electricity retailers and the power consumption of users is given. Finally, the numerical analysis results verify that the proposed scheme can reduce the real-time electricity price and increase the users' satisfaction under feasible constraint, which shows the effectiveness and better performance of proposed RTP scheme.

Mobile edge storage is an emerging computing paradigm which enables users to enjoy high quality of experience when they access cloud services. However, extremely fast increasing multimedia data traffic from cloud to mobile users comes with the challenges of low-latency and high-bandwidth over cloud computing model. How to design cache strategies by exploiting terminal nodes' utility is still a challenging issue. In this paper, by exploiting 5G technology and cooperative edge computing, we propose a cooperative edge caching framework to tackle three issues simultaneously in the application scenarios of massive short video services: (1) reducing data transportation latency to improve mobile uses' quality of experience; (2) mitigating data traffic pressure on backbone network; (3) decreasing the workload on cloud servers. Firstly, we define a cooperative edge caching network model, in which many edge nodes equipped with capacities of storage, user admittance and Internet access form an ad hoc network and each node serves multiple mobile users. Secondly, we design a non-cooperative game model to investigate the cooperation behavior among the edge nodes, we allow each edge node server as a player and make decision of cache (storage) initialization and replacement strategies. Thirdly, we find there is a Nash Equilibrium in the proposed game. Finally, we design a distributed algorithm to achieve the equilibrium according to our theoretical analysis. Experiment results show that the proposed game-based edge caching strategy can improve mobile uses' quality of experience by 20% latency decreasing and reduce 80% backbone traffic and cloud workload.

This paper focus on the two-tier downlink heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA). The aim is to jointly optimize user scheduling and power allocation to solve the problem of maximizing the energy efficiency (EE) of the NOMA HetNets. We formulate the basic principle of NOMA and develop the EE model of joint user scheduling, optimal power allocation factor among users on the same subchannel and power allocation across subchannels. Firstly, considering that the problem of formulation is a multi-objective optimization (MOO) problem, which is non-convex and NP-hard, we decouple the MOO problem into two single-objective optimization (SOO) problems: 1) joint the optimal power allocation factor of maximizing the EE among users on the same subchannel to solves the subchannel matching, and 2) the power allocation across subchannels. For subchannel matching, non-cooperative game and global optimal search (GOS) are respectively used to solve the power allocation factor among users on the same subchannel and user scheduling. For the power allocation factor among users on the same subchannel, the existence of Nash equilibrium (NE) is proved by introducing super-modular game. Besides, the Nash equilibrium point (NEP) of the game can be obtained by using the proposed algorithm. Then, for the correlation between users-subchannels, this paper proposes a non-cooperative game based user-subchannel global optimal search matching algorithm (NCG-US-GOSMA) with low-complexity. Finally, according to the obtained subchannel matching, the non-convex problem of power allocation across subchannels is converted into convex problem by successive convex approximation (SCA), and then solve it by iteration. Simulation results verify the effectiveness of the proposed scheme; in addition, the EE performance is better than the existing resource allocation algorithm and OFDMA scheme.

Vehicular Edge Computing (VEC) is a new leading technology to enhance the vehicular performance through task offloading where resource-confined vehicles offload their computing task to the vehicular multi-access edge computing (MEC) networks in proximity. However, the environment of vehicular task offloading is extremely dynamic and faces some challenges to determine the location of processing the offloaded task. As a result, to achieve optimal performance by using traditional VEC system is difficult because in advance we don't know the demand of vehicles. Therefore, a non-cooperative game theory-based efficient task offloading (NGTO) scheme is proposed in this study where the offloading decisions are taken either the MEC server or remote cloud server through the game-theoretic approach. To reduce the processing latency of the vehicles' computation tasks and assure the maximum utility of each vehicle, we used a distributed best response offloading strategy. Our proposed strategy accommodates its offloading probability to achieve a unique equilibrium under certain conditions. Detailed performance evaluation affirms that our proposed NGTO scheme can outperform in all scenarios. It can minimize the response time at almost 41.2 % and average task failure rate at approximately 56.3% when compared with a local roadside unit computing (LRC) scheme. The reduced response time and task failure rates are approximately 25.2% and 20.4%, respectively, when compared with a collaborative (LRC with cloud via roadside unit) offloading scheme.

We present a model of parallel Levy-driven queues that mix their output into a final product; whatever cannot be mixed is sold on the open market for a lower price. The queues incur holding and capacity costs and can choose their processing rates. We solve the ensuing centralized (system optimal) and decentralized (individual station optimal) profit optimization problems. In equilibrium the queues process work faster than desirable from a system point of view. Several model extensions are also discussed.

The internet of things (IoT) has become an emerging technology owing to the rapidly increasing number of devices and their connectivity to the internet. Routing protocol for low-power and lossy networks (RPL) is a predominant routing protocol for the IoT. The RPL routing embraces the distance to the gateway node as a rank for parent selection and builds the destination oriented directed acyclic graph (DODAG). The malicious nodes utilize the advantage of low rank based parent selection to involve in data forwarding. However, the malicious node behavior and the harsh channel conditions due to the collision are equally considered as malicious in the existing detection mechanisms. In addition, most of the routing solutions exploit the traditional schemes for IoT security, but not considering the adaptability to IoT routing specifications. Thus, this work attempts to integrate the DODAG-specific contextual trust model, and RPL-specific rank variance factor, named as Secure RPL using non-cooperative game MOdels and DEmpster Shaffer Theory in IoT (S-MODEST), such that it can detect the attackers accurately and significantly reduces the resource consumption. The non-cooperative game model consists of two interrelated formulations in the proposed solution. Firstly, non-zero sum game constructs the trust model and extracts the contextual information from DODAG structure to strengthen the trustworthiness. Secondly, the evolutionary game is utilized for trusted router selection. The non-zero sum game theory formulates the interaction of nodes and assists the trust measurement by applying direct and restricted Dempster–Shaffer theory. The DODAG-specific contextual trust evaluation differentiates the collision dropping from the undesirable scenarios and supports the nodes in building a highly trusted DODAG structure to the gateway using multiplicative trust factor. To enforce the routing cooperation of selfish nodes and differentiate malicious nodes, the evolutionary game model embraces the node trust, which is measured using non-zero sum game model. As a result, the non-cooperative game models enable the S-MODEST to precisely select a secure route as well as to maintain the routing performance effectively. The simulation results demonstrate that the detection accuracy and throughput of the proposed context-aware noncooperative game theory based trust model are substantially higher and outperform the existing SecTrust.

In the present study the concept of non-cooperative game theory is proposed in the retail electricity market for introducing stepwise power tariff model (SPT) for agricultural consumers. The objective of the paper is to increase the energy generation through green energy generation sources (GEGS), introduction of plug-in hybrid electric vehicles, education of families, standard wiring and appliance efficiency in tariffs for agricultural consumers with non-cooperative game theory. Agricultural consumers are able to generate a huge amount of electricity through GEGS and are able to control the consumption in their own way, and the non-cooperative game theory is introduced. Energy consumption pattern varies with respect to time during off-peak load period to peak load period; during the peak load period the demand is high as compared to off peak load hour duration energy consumption for the consumers and policy makers interrupting the energy supply during peak hours for agricultural consumers. To maintain the balance between generation and consumption, energy saving is essentially required and needs to maintain the consumption patterns and increase the penetration level of distributed generation at the agricultural consumer end due to availability of land. This paper proposes an algorithm for a demand response methodology using SPT with non-cooperative game theory model based on monthly energy consumption to maintain the balance. The uncertainty about energy generation through GEGS taken in consideration using Monte-Carlo simulation (MCS). Simulation results obtained by the proposed methodology are compared with the conventional methodology of energy tariff used in India and provide better results for active consumers and generate a considerable amount of electricity through GEGS.

International audience; Recently, solutions based on mobile edge computing paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of unmanned aerial vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay, and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58%, and 55% better results compared to local computing, offloading to the edge server, and offloading to base station, respectively.

The restricted resources of mobile wireless networks such as limited bandwidth have resulted in stringent requirements on the available quality of service (QoS). To enhance the communication performance, mobile nodes attempt to optimize the available bandwidth of wireless networks, which, however, makes them vulnerable to the malicious attacks of adversaries owing to the open and mobile context. Furthermore, improving the security level of the communication consumes bandwidth and thus degrades the communication quality. In order to harmonize the QoS and security, in this paper, the two conflicting terms “communication player” and “security player” are considered as two individual players and are balanced using game theory. Furthermore, we construct a non-cooperative game model, wherein the utilities of the communication player and security player are maximized by controlling the number of nodes transmitted and the length of hash, respectively. We formulate the utility functions of the communication player with respect to the throughput as well as the security level of the Merkle tree for the security player. This paper considers complete information game and incomplete information game, for which we theoretically determine the pure strategy Nash equilibrium and the pure strategy Bayesian Nash equilibrium, respectively. Extensive numerical calculations are performed to comprehensively investigate the utilities of each player and the Nash equilibrium for various combinations of the concerned parameters. The results show that the presented theoretical game approach enables a mobile node to adaptively perform specific actions based on the derived Nash equilibrium. With the obtained Nash equilibrium values, both communication player's and security player's utilities reach to the maximum so that satisfactory communication quality can be realized without compromising the security.

Driven by the increase of data traffic, a heterogeneous ultra-dense network (H-UDN) constitute one of the most promising techniques to support the 5G mobile system. Ultra-dense network (UDN) refers to the idea of densifying the cellular networks to reduce the distance between the access nodes and the user equipment (UE) to achieve the highest possible transmission rates and to enhance the quality of service (QoS). Despite these advantages, (H-UDN) introduces numerous challenges in terms of resource allocation. In this paper, we develop a joint power allocation and user association strategy in H-UDN using non-cooperative game theory. The proposed game is divided into two sub-games, the Backhaul Game is implemented between BS and RNs in the backhaul links and the Access Game is implemented between the BS/RNs and UEs in the access links. The leaders estimate the strategies of their followers to decide on their strategies. Therefore, our solution starts first by solving the users association in the access links to derive the optimal power strategies of the followers and then choosing their optimal power allocation strategies. Subsequently, the followers do the best response to the leaders’ strategies. The simulation results show that our proposed algorithm can achieve the optimal power allocation and improve the performances of the system in term of throughput and UE rate compared to existing methods.

In internet market, content providers (CPs) continue to play a primordial role in the process of accessing different types of data. Competition in this area is fierce; customers are looking for providers that offer them good content (credibility of content and quality of service) with a reasonable price. In this work, the authors analyze this competition between CPs and the economic influence of their strategies on the market. The authors formulate their problem as a non-cooperative game among multiple CPs for the same market. Through a detailed analysis, the researchers prove uniqueness of a pure Nash Equilibrium (NE). Furthermore, a fully distributed algorithm to converge on the NE point is presented. In order to quantify how efficient the NE point is, a detailed analysis of the Price of Anarchy (PoA) is adopted to ensure the performance of the system at equilibrium. Finally, an extensive numerical study is provided to describe the interactions between CPs and to point out the importance of quality of service (QoS) and credibility of content in the market.

The development of smart grids and photovoltaic (PV) generation has led to the popularization and access of distributed PV energy among residential communities. However, the interactions among residential customers significantly affect the power utilization strategy of PV consumption. To encourage customers to participate in PV consumption, this study proposes an optimal smart power utilization model by using a non-cooperative game for a residential community with distributed PV energy. First, a benefit maximization model for distributed PV energy is established to determine an optimal PV output. Second, according to the consumption habits and load curves of residential customers, a power utilization model of community customers is built considering electricity consumption satisfaction by performing clustering analysis. Finally, a non-cooperative game model is built for the PV power supplier and customers in the community. A Nash equilibrium point is also obtained based on the balance between the maximum benefit of PV power and the minimum electricity bills for customers. This approach is useful to encourage customers to consume PV energy in local areas. The case studies indicate that the proposed model can be applied to achieve the maximum benefit of PV power; furthermore, it allows customers to meet their own needs while reducing their electricity payments.

With the aggravation of global environmental problems, energy saving, and emission reduction are urgent for the manufacturing industry. However, due to the difficulty in coordinating the manufacturing behaviors and profit targets of different subsidiaries, the large complex product manufacturing group is difficult to transform from traditional profit-oriented to green-oriented. To this end, this paper proposes two production planning optimization strategies for the manufacturing group. One is the profit-optimized strategy that optimizes the cost and makespan of the group manufacturing process. The other is the energy-optimized strategy that adds the energy consumption objective to the group's original objectives. Meanwhile, the potential benefit conflict of different strategies is considered in this paper. The non-cooperative game is introduced to analyze the benefit allocation under the two strategies and formulate a reward mechanism to encourage the subsidiaries to participate in the energy-optimized strategy. Then, a novel hybrid MONSGA-II&MSNE approach is presented to address the integrated problem. To effectively optimize the two strategies, the MONSGA-II is improved by the multi-oriented optimization heuristic strategy in two aspects: 1) enhancing the local search ability of NSGA-II and 2) solving the matching conflict problem in the crossover operation. The mixed strategy Nash equilibrium (MSNE) is applied to calculate the cost-optimal reward mechanism under different game combinations. Finally, the performance of the proposed approach is demonstrated in the case study.

In device-to-device (D2D) communications underlaying cellular systems based on the cloud radio access network, including distributed remote radio heads (RRHs) and centralized BaseBand Unit (BBU) pool, due to dense deployment and spectrum reuse, there are strong intra-cell interference and inter-cell interference, which will severely degrade both the energy efficiency (EE) and the quality of service. To address this problem, we propose an energy-efficient resource allocation scheme to determine channel selection and power allocation by introducing the relay-aided in-band D2D communication mode and the non-cooperative game theory. We explore the above resource allocation problem in a hybrid manner and model it as a non-cooperative game, where each player optimizes its EE individually with the aid of RRHs. Then, we alleviate the inter-cell interference from the cellular UEs near the cell edge by allowing these UEs to select in-band D2D relay links to shorten the long transmission distances between them and RRHs. Moreover, the interfering signal processing load of the BBU is lowered due to the reduction of the interfering signal strength, and the EE is improved due to the reduction of transmission powers of these cellular UEs. The achievable performance of the proposed scheme is analyzed through simulations.

We formulate a network security problem as a zero-sum game between an attacker who tries to disrupt a network by disabling one or more nodes, and the nodes of the network who must allocate limited resources in defense of the network. The utility of the zero-sum game can be one of several network performance metrics that correspond to node centrality measures. We first present a fast centralized algorithm that uses a monotone property of the utility function to compute saddle-point equilibrium strategies for the case of single-node attacks and single- or multiple-node defense. We then extend the approach to the distributed setting by computing the necessary quantities using a finite-time distributed averaging algorithm. For simultaneous attacks to multiple nodes the computational complexity becomes quite high, so we propose a method to approximate the saddle-point equilibrium strategies based on a sequential simplification, which performs well in simulations.

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which ’ON’ and ’OFF’ duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors $\varsigma = 0.02$ ς = 0 . 02 , the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

In a heterogeneous wireless network (HWN) environment, performing an efficient vertical handoff requires the efficient qualitative evaluation of all stakeholders like wireless networks (WN) and mobile users (MU) and mutual selection of best WN-MU. In the literature, most of the work deals with both these requirements jointly in the techniques proposed by them for the vertical handoffs (VHO) in HWNs, leaving very little scope to manipulate the above requirements independently. This may result in inefficient vertical handoffs. Hence, this chapter proposed a generalized two-stage two players, iterative non-cooperative game model. This model presents a modular framework that separates the quantitative evaluation of WNs and MUs (at Stage 1) from the game formulation and solution (at Stage 2) for mutual selection of best WN-MU pair for VHO. The simulation results show a substantial reduction in the number of vertical handoffs with the proposed game theory-based two-stage model as compared to a single-stage non-game theory method like multiple attribute decision making.

This manuscript deals with the situation in which the different agents or buses in a power network have access to local renewable resources and must manage its use in a distributed fashion. The buses distributedly decide the amount of power to be generated using their local renewable power plants, and that to be demanded from the grid. The decisions are made according to the optimization of a cost function that considers both economic and technical factors. The problem is approached resorting to a game-theoretical framework that requires a negotiation process among the neighboring buses. An iterative algorithm is proposed in order to solve this problem, providing existence and convergence conditions under which the buses reach a suitable equilibrium. The algorithm performance is tested in simulations over a modification of the IEEE 14-bus system, in which the lines are modeled as resistances and distributed generation is considered. Simulations on a network of 44 buses are also included to show the scalability of the method. Unión Europea FEDER TEC2016-80242-P

To address the problems in end-to-end networks, researchers have proposed an information-centric network(ICN). In-network caching is one of the important features of ICN, which can greatly improve the efficiency of content distribution. However, the Internet Service Providers(ISP) only deploy Innetwork caching if there is some financial incentives. The existing ICN pricing mechanisms only study paid content but ignore free content in the network. In addition, the existing ICN economic models do not take into account ISP cooperating with Content Provider(CP) to deploy in-network caches. Therefore, the pricing mechanisms already in place cannot be applied in practice. In this paper, different from the existing models which based on the paid content, a new ICN pricing model with free content based on non-cooperative game theory was proposed for the first time and advertisers were effectively added to the ICN pricing model. After analyzing the behavioral relationships between entities within the network, the authors establish the utility function for each entity. The authors analyze the impact of caching and pricing on the revenues of all entities and solve the equilibrium point to develop win-win pricing strategies. Considering the case where paid content coexists with free content in real networks, the authors proposes a more realistic and integrated model. In this paper, many experimental analyses have been conducted in addition to the theoretical analysis, and the experimental results show that the new model is closer to reality than the existing models and can provide better theoretical guidance for network pricing.

The diversification of market subjects has become one of the most significant characteristics of power grids with the opening of power market policies. According to the theory of non-cooperative complete information game, this study focused on distributed generation (DG) operators, the distribution network operator (DNO) and power users. Considering the system economy and the power consumption satisfaction, a multi-agent collaborative planning method for DGs depended on non-cooperative games is proposed to achieve stable and friendly interaction between agents. CPLEX is used to solve the model to obtain the location and capacity of DGs. The simulation results indicated that the proposed method could balance the economic benefits of the main agents, and improve user comfort effectively.

Aiming at the fairness and economy of power system dispatching, considering the maximum profit of each generation unit, a non cooperative game model with wind power, photovoltaic power and pumped storage power station as game participants is established. This model takes the income of power generation unit as the optimization objective, uses Gini coefficient to represent fairness and takes it as one of the constraints to establish a mathematical model. CPLEX is used to solve the models in three different scenarios and verify the correctness and effectiveness of the models. The results show that the addition of pumped storage power station can not only improve the utilization rate of renewable energy, but also increase the system income; when the Nash equilibrium condition is reached, the total system income is higher than that under general conditions; the increase of Gini coefficient will lead to the increase of generation cost of dispatching system, while the economic benefit will be relatively reduced.

Transmission Control Protocol (TCP) is used for delivering internet data to users. TCP is prone to network congestion and its ability to combat network congestion has certain limitations, especially in the case of wireless networks. Currently, Long Term Evolution (LTE) is one of the main chosen standards for mobile and fixed wireless data communications. LTE uses various error correction techniques for reliable data delivery over the air-interface. These cause other issues such as excessive latency due to queuing in the Base Station (BS). Therefore, adequate congestion mitigation mechanisms are required. Existing mechanisms such as Discard Timer (DT) and Random Early Detection (RED) require parameter configuration and are therefore limited in their ability to mitigate congestion. In this paper, a new mechanism has been proposed of implementing congestion management using radio resource allocation techniques with Game Theory as an underlying mathematical framework.

The growth of connected vehicles in smart cities increases the number of information being communicated on the Internet of Vehicle networks. This causes wireless channel congestion problems, which degrades the network performance and reliability due to the low throughput, high average delay and the high packets loss. Therefore, this paper proposes a non-cooperative game approach to control congestion in the vehicular ad-hoc network channel where the nodes behave as selfish players requesting high data transmission rates. Moreover, the satisfaction of the Nash equilibrium condition for the optimum data transmission rate for each vehicle, is proven. A utility function is introduced based on data transmission rates, the priority of vehicles and contention delay in order to obtain the optimal rates. The performance of the proposed approach has been evaluated and validated in comparison with three others approaches over two testing scenarios for highway and urban traffic. The results show that the network performance and efficiency have been improved by an overall average of 35%, 30% and 37.17% in terms of packets loss, channel busy time and number of collision messages, respectively, as compared with the state-of-the-art-strategies for the highway testing scenario. Similar performance is achieved for the urban testing scenario. Keywords: Vehicular ad hoc networks, Congestion control, Non-cooperative game approach, Data rate adaptation, IoV applications.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

Aiming at the problems that today’s distributed trading market does not fully reflect the energy consumption characteristics of terminal users. The game relationship between market players yet merits further research. This paper proposed a distributed Peer-to-Peer (P2P) trading method for microgrid clusters in active distribution networks containing series of integrated smart buildings. The smart building was first taken as the carrier of the terminal load in a thermal balance model entitled “thermal resistance-heat capacity (R-C) An autonomous economic scheduling model of the market entity was then established accordingly. The non-cooperative game model and the Stackelberg game model were used to analyze the game relationship among sellers and between sellers and buyers in P2P transactions. The impact of terminal energy consumption characteristics on microgrid economic scheduling and P2P transactions was analyzed under real-world conditions; the economic and technological benefits remitted by P2P transactions on microgrid operation were also quantitatively analyzed.

This paper proposes a double auction-based mechanism that captures the interaction within a community consisting of distributed solar power prosumers and consumers. All agents are assumed to have battery energy storage systems, and can use battery for time-of-use arbitrage. Sellers and buyers can optimize the charging/discharging schedules of their battery systems for community sharing to reduce electricity costs. To determine the market clearing price, a non-cooperative game is formulated among all participants involved in the community sharing. To solve the game, an iterative algorithm is first designed to determine the clearing price and energy trading amount during each round of auction. Then, an adaptive pricing strategy is designed to assist agents better understand the market. A case study with 10 agents is provided to demonstrate the effectiveness of the proposed community sharing market.

This paper presents a novel two-level Stackelberg game strategy to model the competitive relationship between power supply companies, integrated energy systems, and end users in energy trading. In the upper-level game, the power supply company acts as a leader, which stimulates the electricity purchase of the IES by adjusting the electricity price. While the integrated energy system serves as a follower, whose objective is to minimize the operational cost and determine the power purchased according to the electricity price. In the lower-level game, the integrated energy system behaves as a leader, and it sets the energy sales price on the basis of the actual energy demand of the user. As a follower, end users adjust their energy consumption strategies to balance energy purchase expenditure and energy use experience. The proposed two-level game model well describes the competition among the three participants. In order to solve the model, a distributed algorithm using alternating iterations is presented. Numeric studies have demonstrate that the developed game strategy can obtain the equilibrium solution of each participant, and can provide a good reference for energy system trading and operation.

The restricted resources in IoT networks such as limited battery have resulted in strict requirements to prolong the network life time. To improve the communication, IoT nodes attempt to optimize the available energy in the sensor network, this makes them vulnerable to the malicious attacks from adversaries because of open scenario. In addition, enhancing the security level will consume the energy and decreases network life time. In order to balance energy and security in the network game theory concept is used. We design a non cooperative game between energy and security where the utilities of both energy and security players are maximized by controlling the number of nodes transmitting and hash length. We consider complete and incomplete information game and determine Nash equilibrium. Extensive simulation have been performed to examine Nash equilibrium. We obtained Nash equilibrium for both energy and security players.

One of the key issues in Vehicular Ad-hoc networks (VANETs) is to optimize the traffic congestion. Cooperation in these networks is a challenging issue due to their specific characteristics. In this paper, a non-cooperative game theory-based approach is introduced for packet forwarding. Through extensive mathematical analyses and also experimental validation, we prove that the proposed non-cooperative game mechanism attains the Nash equilibrium point. Our designed mechanism encourages all vehicles to collaborate with each other in packet forwarding operations. This, in turn, results in decreasing the payments by nodes to the network side and also results in optimizing traffic congestion. The simulation results established the robustness of the proposed mechanism in terms of cost-related criteria.

In this paper, we study decentralized computation offloading over the wireless powered mobile edge computing. We first build a decentralized game (named by mobile edge computing game based on wireless power) model for multiple users who need to execute the computation offloading assisted by the access point's wireless power. For achieving efficient computation offloading, the game aims to obtain the optimal system computing cost including weighted harvested energy and offloading delay. Then, we analyze computation offloading decision for the proposed wireless powered computation offloading game theoretical and show that the theoretical can admit a Nash equilibrium (NE). Finally, numerical results demonstrate that the proposed computation offloading game theoretical can improve computation offloading performance, observably.

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

Cognitive radio (CR) is a form of wireless communication, that provides solution for the spectrum underutilization problems via dynamic spectrum sharing among primary users and secondary users. In cognitive radio network, interference and power consumption must be carefully controlled to maximize the channel usage. Game theory has emerged in the last fifteen years as an effective framework for communications problems, which is used to analyze those problems and help to derive solutions. In this paper, we propose a sufficiently opportunistic utilization for spectrum resources by solving two challenges: interference and power consumption. We analyze the spectrum allocation problem under game theoretical framework and we propose an efficient algorithm to examine the design specification issues regarding the choice of optimal power, optimal speed, and optimal amount of information in a wireless network. Our objectives are to regulate the opportunistic spectrum access, by the secondary users in order to guarantee the protection on licensed users from harmful interference. More especially, to optimize the quality of communication link, transmission levels, and battery life of the wireless devices.

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach., Comment: 5 pages, 3 figures. This is an accepted paper and it is going to appear in the Proceedings of the 2020 IEEE International Conference on Web Services (IEEE ICWS 2020) affiliated with the 2020 IEEE World Congress on Services (IEEE SERVICES 2020), Beijing, China

With the rapid development of the next generation mobile network, terminal equipment and data traffic are growing rapidly, and the developing trend of the communication network is presents intelligence and large capacity. D2D (device-to-device) communication sharing spectrum resources with cellular users is being developed rapidly, for it can effectively solve the problem of large-scale user terminal access, improve the utilization rate of spectrum resources, and expand the coverage of the network. A D2D power control algorithm with price control based on non-cooperative game theory is proposed to solve the interference problem in the hybrid network system of cellular users and D2D users. Firstly, the utility function is established by analyzing the benefit and utility of D2D users and the interference to the cell base station, and by considering the service quality of the cell users and constraint of the interference tolerance of the base station to the transmission power. Then, by using the interference size of D2D users to cellular users, base station updates the interference price of the cellular system. Accordingly, the D2D users adjust the transmission power and maximize the profit and maximizes the overall profit of the system by using the Lagrange multiplier method to find the price. Next, the existence and uniqueness of Nash equilibrium solution are proved by taking Nash equilibrium as the result of non-cooperative game. Finally, numerical simulation results show that the algorithm can effectively control the transmission power of D2D users and effectively improve the total rate of D2D networks.

In order to promote the development of energy storage industry and relieve the retirement pressure of EV batteries, the application of retired EV batteries in energy storage for second-life use has gained significant attentions. Considering that distributed battery storages are still not economically attractive enough at present, the booming of sharing economy may provide a more feasible option. Based on a non-cooperative game theoretical model, this study aims to explore the impact of sharing business models on economic performance of retired EV batteries integrated with distributed PV systems. Four scenarios were proposed, including: a traditional scenario of S1 - individual battery storages, the sharing scenarios of S2 - shared battery storage, S3 - shared PV generation, and S4 - shared battery storage and shared PV generation. Through a case study in Beijing, the obtained results show that: (i)although sharing business models may not guarantee to bring economic benefits to each individual, they can contribute to a reduction in electricity expenses of the whole community; (ii) sharing business models can enhance PV self-consumption ratio and battery utilization ratio; (iii) sharing business models can improve the NPV by 1995 RMB-6975 RMB compared to the traditional scenario.