Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases

A Blockchain-Based Reward Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS

Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: An Integration of Blockchain and Edge Computing

Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand-supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex-concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader-follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis

Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

Strategic decision-making on low-carbon technology and network capacity investments using game theory

In recent years, renewable energy technologies have been increasingly adopted and seen as key to humanity’s efforts to reduce greenhouse gases emissions and combat climate change. Yet, a side effect is that renewables have reached high penetration rates in many areas, leading to undesired curtailment, especially if existing grid infrastructure is insufficient and renewable energy generated cannot be exported at areas of high energy demand. The issue of curtailment is compelling at remote areas, where renewable resources are abundant, such as in windy islands. Not only renewable production is wasted, but often curtailment comes with high costs for renewable energy developers and energy end-users. In fact, procedures on how generators access the grid and how curtailment is applied, are key factors that affect the decisions of investors about generation and grid capacity installed. Part of this thesis studies the properties of widely used curtailment rules, applied in several countries including the UK, and their effect on strategic interactions between self-interested and profit-maximising low-carbon technology investors. The work develops a game-theoretic framework to study the effects of curtailment on the profitability of existing renewable projects and future developments. More specifically, work presented in this thesis determines the upper bounds of tolerable curtailment at a given location that allows for profitable investments. Moreover, the work studies the effect of various curtailment strategies on the capacity factor of renewable generators and the effects of renewable resource spatial correlation on the resulting curtailment. In fact, power network operators face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this context, this thesis shows that fairness and equal sharing of imposed curtailment among generators is important to achieve maximisation of the renewable generation capacity installed at a certain area. A new rule is proposed that minimises disruption and the number of curtailment events a generator needs to respond to, while achieving fair allocation of curtailment between generators of unequal ratings. While curtailment can be reduced by smart grid techniques, a long term solution is increasing the network capacity. Grid reinforcements, however, are expensive and costs weight to all energy consumers. For this reason, debate in the energy community has focused on ways to attract private investment in grid reinforcement. A key knowledge gap faced by regulators is how to incentivise such projects, that could prove beneficial, especially in cases where several distributed generators can use the same power line to access the main grid, against the payment of a transmission fee. This thesis develops methods from empirical and algorithmic game theory to provide solutions to this problem. Specifically, a two-location model is considered, where excess renewable generation and demand are not co-located, and where a private renewable investor constructs a power line, providing also access to other generators, against a charge for transmission. In other words, the privately developed line is shared among all generators, a principle known as ‘common access’ line rules. This formulation may be studied as a Stackelberg game between transmission and local generation capacity investors. Decisions on optimal (and interdependent) renewable capacities built by investors, affect the resulting curtailment and profitability of projects and can be determined in the equilibrium of the game. A first approach to study the behaviour of investors at the game equilibrium, assumed a simple model, based on average values of renewable production and demand over a larger time horizon. This assumption allowed for an initial examination of the Stackelberg game equilibrium, by achieving an analytical, closed-form solution of the equilibrium and the investigation of its properties for a wide range of cost parameters. Next, a refined model is developed, able to capture the stochastic nature of renewable production and variability of energy demand. A theoretical analysis of the game is presented along with an estimation of the equilibrium by utilisation of empirical game-theoretic techniques and production/demand data from a real network upgrade project in the UK. The proposed method is general, and can be applied to similar case studies, where there is excess of renewable generation capacity, and where sufficient data is available. In practice, however, available data may be erroneous or experience significant gaps. To deal with data issues, a method for generating time series data is developed, based on Gibbs sampling. This attains an iterative simulation analysis with different time series data as an input (Markov Chain Monte Carlo), thus achieving the exploration of the solution space for multiple future scenarios and leading to a reduction of the uncertainty with regards to the investment decisions taken. Energy storage can reduce curtailment or defer network upgrades. Hence, the last part of this thesis proposes a model consisted of a line investor, local generators and a third independent storage player, who can absorb renewable production, that would otherwise have been curtailed. The model estimates optimal transmission, generation and storage capacities for various financial parameters. The value of storage is determined by comparing the energy system operation with and without energy storage. All models proposed in this thesis, are validated and applied to a practical setting of a grid reinforcement project, in the UK, and a large dataset of real wind speed measurements and demand. In summary, the research work studies the interplay among self-interested and indepen dent low-carbon investors, at areas of excess renewable capacity with network constraints and high curtailment. The work proposes a mechanism for setting transmission charges that ensures that the transmission line gets built, but investors from the local community, can also benefit from investing in renewable energy and energy storage. Overall, the results of this work show how game-theoretic techniques can help energy system stakeholders to bridge the knowledge gap about setting optimal curtailment rules and determining appropriate transmission charges for privately developed network infrastructure.Engineering and Physical Sciences Research Council (EPSRC

A Comprehensive Insight into Game Theory in relevance to Cyber Security

The progressively ubiquitous connectivity in the present information systems pose newer challenges tosecurity. The conventional security mechanisms have come a long way in securing the well-definedobjectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in thesystem complexities and attack sophistication, providing security via traditional means can beunaffordable. A novel theoretical perspective and an innovative approach are thus required forunderstanding security from decision-making and strategic viewpoint. One of the analytical tools whichmay assist the researchers in designing security protocols for computer networks is game theory. Thegame-theoretic concept finds extensive applications in security at different levels, including thecyberspace and is generally categorized under security games. It can be utilized as a robust mathematicaltool for modelling and analyzing contemporary security issues. Game theory offers a natural frameworkfor capturing the defensive as well as adversarial interactions between the defenders and the attackers.Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategiesof attackers by equilibrium evaluation of the security games. In this paper, the concept of game theoryhas been presented, followed by game-theoretic applications in cybersecurity including cryptography.Different types of games, particularly those focused on securing the cyberspace, have been analysed andvaried game-theoretic methodologies including mechanism design theories have been outlined foroffering a modern foundation of the science of cybersecurity

Blockchain systems, technologies and applications: a methodology perspective

In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of things, etc