Coalitional Game Research Articles

CBP: Coalitional-Game-Based Broadcast Proxy Re-Encryption in IoT

This paper proposes Coalitional Game-Based Broadcast Proxy Re-encryption in IoT a broadcast proxy re-encryption for adding new IoT devices. The proxy re-encryption is extended to broadcast proxy re-encryption to prevent re-calculation of the re-encryption key. However, the group of recipients needs to be pre-determined before the calculation of the re-encryption key. If any new IoT device requires the same data, an individual re-encryption key is generated for him/her. Hence, generating individual re-encryption key is an overhead for the organization. We propose a re-encryption key updation for the broadcast proxy re-encryption method. If excessive IoT devices want to join the group of the existing recipient, then updation needs to be done learnedly as an excessive number of recipients in a group increases the computation cost of the decryption extremely for all the members of the group. Therefore, we use the coalitional game theory to estimate the optimal number of new recipients from all new recipients. We update the re-encryption key for the optimal number of members and a separate re-encryption key is calculated for other recipients. We prove the correctness of the CBP. We prove that if any recipient behaves maliciously, s/he cannot get the secret key of the organization.

Corporate social responsibility in the global value chain: A bargaining perspective

Breaches of corporate social responsibility (CSR) in global value chains (GVCs) pose a managerial challenge for multinational enterprises (MNEs) and threaten both their reputations and global sustainability. While an MNE-centric perspective on these issues has dominated existing international business research, we show that a dynamic view of bargaining among actors in the GVC can yield novel insights. We draw on coalitional game theory and develop a model where an MNE collaborates, monitors, and negotiates prices with a supplier whose CSR breaches may be revealed by the MNE, external agents, or remain hidden. Our model illustrates how MNEs may face a hold-up problem when irresponsible actions by suppliers are made public, and the suppliers have the power to engage in opportunistic renegotiation. Interestingly, we show that greater monitoring by MNEs, if not combined with specific strategies, can have negative consequences by weakening the MNE’s bargaining position and, in some cases, even prompting more irresponsible actions by the suppliers. Our model advances international business research on GVC sustainability and has important implications for managers and researchers alike.

Joint optimization of resource allocation and computation offloading based on game coalition in C-V2X

In order to solve the problem of limited resources in the vehicle, mobile edge computing (MEC) is integrated into the cellular-vehicle-to-everything (C-V2X) system to provide users with low-latency services. Considering the resource limitation of computing, storage and communication of MEC servers, it is necessary to improve the system performance through the collaborative optimization of multi-domain resources. Therefore, for applications with double dependence on time and data in C-V2X scenarios, we design a MEC hierarchical resource management framework to jointly optimize system offloading decision, scheduling decision, and caching decision to minimize the system delay. The optimization problem is decomposed into a resource allocation problem within a single MEC server and a load balancing problem among multiple MEC servers. For the former, we present a task scheduling algorithm based on the latest start time and a caching decision algorithm based on dynamic programming to minimize the average task completion delay; for the latter, we present a load balancing algorithm based on the coalition game to minimize the global system delay. Simulation results show that the proposed scheme can significantly improve the performance in terms of application completion delay, application failure rate and resource utilization compared with the benchmark schemes.

Coalition Game Theoretic Power Allocation Strategy for Multi-Target Detection in Distributed Radar Networks

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm.

Shared Energy and Reserve Based Coordinated Operation Between Electric Vehicle Charging Stations and Distribution Power Network

In this paper, we propose a coordinated operation method for a system containing electric vehicle charging stations (EVCSs) and a distribution power network (DPN), considering integrated energy and reserve regulation. In this method, we firstly propose a multi-objective optimization model of energy-reserve integrated decisions for the system considering the economic cost and the quality of service (QoS), which is based on the network calculus theory. Then, we adopt a characteristic function and a modified Shapely value method based on the coalition game to study allocations of economic benefits brought by the coordinated operation. Finally, simulation studies verify the effectiveness of the proposed method, by investigating the operation costs of EVCSs and the DPN in the non-coordinated and coordinated modes, respectively.

Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients

In this work, we propose a new paradigm of Federated Learning (FL) for Internet of Things (IoT) devices called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Coalitional Federated Learning</i> . The proposed paradigm aims to address the challenges of (1) non-independent and identically distributed (non-IID) data across clients; (2) communication overhead due to the large number of messages exchanged between the server and clients; and (3) selfish clients that seek to obtain the latest global models without efficiently contributing to the training of the FL model. Our novel paradigm consists of three main components, i.e., (1) client-to-client trust establishment mechanism that relies on subjective and objective sources to enable clients to establish credible trust relationships toward one another; (2) trust-enabled coalitional game to enable clients to autonomously form harmonious coalitions of FL trainers; and (3) coalitional federated learning in which multiple local aggregations take place at the level of each coalition to mitigate the problems of non-IID data and communication bottleneck. Extensive experiments suggest that our solution outperforms both the standard vanilla FL approach and one state-of-the-art trust-based FL approach in terms of increasing the accuracy of the global FL model and decreasing the presence of selfish devices participating in the training.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Some Properties of Interval Shapley Values: An Axiomatic Analysis

Interval games are an extension of cooperative coalitional games, in which players are assumed to face payoff uncertainty. Characteristic functions thus assign a closed interval instead of a real number. This study revisits two interval game versions of Shapley values (i.e., the interval Shapley value and the interval Shapley-like value) and characterizes them using an axiomatic approach. For the interval Shapley value, we show that the existing axiomatization can be generalized to a wider subclass of interval games called size monotonic games. For the interval Shapley-like value, we show that a standard axiomatization using Young’s strong monotonicity holds on the whole class of interval games.

HARMONIC: Shapley values in market games for resource allocation in vehicular clouds

Real-time allocation of resources to fulfill service requests from road vehicles is becoming increasingly complex, for two main reasons: the continuous increase in the number of Internet-connected vehicles on roads all over the world, and the emergence of complex and resource-greedy applications that require fast execution, often under limited availability of computational resources. While many resource allocation solutions to this problem have been proposed recently, these solutions rely on unrealistic scenarios and constraints that limit their practical use.This paper presents HARMONIC, a Game Theory-based coalition game that aims to maximize resource utilization and dynamically balance resource usage across multiple Vehicular Clouds (VCs). HARMONIC employs a Shapley value-based strategy to determine the order of task allocation to available resources. It is built upon our proposed Market Game model, specifically designed to address resource allocation challenges in dynamic VCs. We conduct a comparative analysis with existing literature solutions under various scenarios and resource constraints to evaluate HARMONIC’s performance. Our simulation results demonstrate that HARMONIC achieves resource allocation in fewer rounds and with fewer failures. Furthermore, it effectively distributes tasks to more VCs, improving load balancing and overall system efficiency.

Coalition Game of Radar Network for Multitarget Tracking via Model-Based Multiagent Reinforcement Learning

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational cost, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiagent decision-making and learning</i> problem, where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interact with other agents for the sake of the best network utility. To describe the decision references of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coalition game with transferrable utility</i> is developed for task assignment, where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random Fourier features reinforcement learning</i> (RFFRL) algorithm is proposed to solve the optimal solution to the game in the high dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.

Blockchain-Aided Edge Computing Market: Smart Contract and Consensus Mechanisms

Building upon the concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and show that the proposed mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness-driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair reward allocation. We formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities.

Online Coalitional Games for Real-Time Payoff Distribution With Applications to Energy Markets

Online Coalitional Games for Real-Time Payoff Distribution With Applications to Energy Markets

The coalitional aggregation of wind energy: A stochastic duality approach

The inherent uncertainty of wind power poses a major challenge to modern electricity grid. This paper mainly investigates a scenario where a crowd of wind power producers form a coalition by cooperatively offering their aggregated power output, aiming to mitigate these risks created by the inherent uncertainty of wind power. The main results are as follows. Firstly, a stochastic two-stage programming problem is proposed to formulate the capacity management with respect to a wind power producers’ coalitional game. Secondly, we give a constructive proof to illustrate that a wind power producers’ coalitional game has a nonempty core using the theory of cooperative game. Meanwhile, we also show that the form of a coalition always reduces their expected costs. Thirdly, we propose a stochastic duality approach to design a fair cost allocation scheme for players of a wind power producers’ coalition. Finally, we consider a concave function structure, indicating that there exists economies of scale, and extend this proposed duality theory to the wind power producers’ coalitional game under a concave capacity investment cost structure. Furthermore, we find that a wind power producers’ coalition is balanced under the concave capacity investment cost function. Our results provide an incentive for collaboration strategies in wind power setting, and promote sustainable development of wind power.

Bilateral Peer-to-Peer Energy Trading via Coalitional Games

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of &#x201C;stable&#x201D; and &#x201C;fair&#x201D; bilateral contracts which encourages prosumer participation. The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.

Effect of alteplase, benzodiazepines and beta-blocker on post-stroke pneumonia: Exploration of VISTA-Acute.

Post-stroke pneumonia is a frequent complication of stroke and is associated with high mortality. Investigators have described its associations with beta-blocker. However, there has been no evaluation of the role of recombinant tissue plasminogen activator (RTPA). We postulate that RTPA may modify the effect of stroke on pneumonia by reducing stroke disability. We explore this using data from neuroprotection trials in Virtual International Stroke Trials Archive (VISTA)-Acute. We evaluated the impact of RTPA and other medications in random forest model. Random forest is a type of supervised ensemble tree-based machine learning method. We used the standard approach for performing random forest and partitioned the data into training (70%) and validation (30%) sets. This action enabled to the model developed on training data to be evaluated in the validation data. We borrowed idea from Coalition Game Theory on fair distribution of marginal profit (Shapley value) to determine proportional contribution of a covariate to the model. Consistent with other analysis using the VISTA-Acute data, the diagnosis of post-stroke pneumonia was based on reports of serious adverse events. The overall frequency of pneumonia was 10.9% (614/5652). It was present in 11.5% of the RTPA (270/2358) and 10.4% (344/3295) of the no RTPA groups. There was significant (p<0.05) imbalance in covariates (age, baseline National Institutes of Health Stroke Scale (NIHSS), diabetes, and sex). The AUC for training data was 0.70 (95% CI 0.65-0.76), validation data was 0.67 (95% CI 0.62-0.73). The Shapley value shows that baseline NIHSS (≥10) and age (≥80) made the largest contribution to the model of pneumonia while absence of benzodiazepine may protect against pneumonia. RTPA and beta-blocker had very low effect on frequency of pneumonia. In this cohort pneumonia was strongly associated with stroke severity and age whereas RTPA had a much lower effect. An intriguing finding is a possible association between benzodiazepine and pneumonia but this requires further evaluation.

Interpretable spectroscopic modelling of soil with machine learning

AbstractSpectroscopic modelling of soil has advanced greatly with the development of large spectral libraries, computational resources and statistical modelling. The use of complex statistical and algorithmic tools from the field of machine learning has become popular for predicting properties from their visible, near‐ and mid‐infrared spectra. Many users, however, find it difficult to trust the predictions made with machine learning. We lack interpretation and understanding of how the predictions were made, so that these models are often referred to as black boxes. In this study, I report on the development and application of a model‐independent method for interpreting complex machine learning spectroscopic models. The method relies on Shapley values, a statistical approach originally developed in coalitional game theory. In a case study for predicting the total organic carbon from a large European mid‐infrared spectroscopic database, I fitted a random forest machine learning model and showed how Shapley values can help us understand (i) the average contribution of individual wavenumbers, (ii) the contribution of the spectrum‐specific wavenumbers, and (iii) the average contribution of groups of spectra taken together with similar characteristics. The results show that Shapley values revealed more insights than commonly used interpretation methods based on the variable importance. The most striking spectral regions identified as important contributors to the prediction corresponded to the molecular vibration of organic and inorganic compounds that are known to relate to organic carbon. Shapley values are a useful methodological development that will yield a better understanding and trust of complex machine learning and algorithmic tool in soil spectroscopy research.

Values of games over Boolean player sets

In this paper, we study new classes of value operators for coalitional games with players organized into a boolean algebra. Coalitional games are cooperative models in which players can form coalitions to maximize profit. The basic solution concepts in such game scenarios are value operators, which assign a unique real value to every player, reflecting thus selected principles of economic rationality. Some value concepts were extended beyond the classic coalitional model where every coalition of players can form. In particular, the extension of Shapley value exists for coalitional games in which players are partially ordered, and the feasible coalitions are the corresponding down-sets. Interestingly, this game-theoretic framework was employed in the method called Information Attribution. This method aims to solve the information decomposition problem, which asks for a particular additive decomposition of the mutual information between the input and target random variables. In such information-theoretic games, the players are predictors, and their set has the natural structure of a boolean algebra. Motivated by the original problem, we consider coalitional games where the players form a boolean algebra, and the coalitions are the corresponding down-sets. This more general approach enables us to study various value solution concepts in detail. Namely, we focus on the classes of values that can represent alternatives to the solution of the information decomposition problem, such as random-order values or sharing values. We extend the axiomatic characterization of some classes of values that were known only for the standard coalitional games.

Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells Using Game Theory.

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE.

Energy Minimization for NOMA Cellular Networks With Two-Dimensional Resource Allocation

In this paper, we investigate the energy minimization for the cellular networks with non-orthogonal multiple access (NOMA). Different from most of existing studies in NOMA networks that considered the resource allocation in only frequency domain or time domain, we discuss the two-dimensional time-frequency communication resource allocation. We formulate the considered problem as a mixed integer non-linear programming (MINLP), and decompose it into three nested subproblems: transmit power optimization, communication resource allocation, and NOMA group partition. Then, we solve them iteratively. For the transmit power optimization subproblem, we first propose a transmit power iteration (TPI) algorithm, after the simplification of the rate requirement constraint, to obtain the transmit powers of cellular users (CUs) in the special case such that a group of CUs only occupies the communication resource in one sub-channel, and prove its optimality and convergence theoretically. Then, a rate requirement allocation based TPI (RRA-TPI) algorithm is further proposed to obtain the transmit powers of CUs in the general cases. For the communication resource allocation and NOMA group partition subproblems, a two-dimensional resource allocation (TDRA) algorithm and a coalition game (CG) algorithm are proposed to solve them, respectively. Simulation results show that via two-dimensional time-frequency resource allocation, we can achieve a significant system energy reduction.

Mobile Device Association and Resource Allocation in HCNs With Mobile Edge Computing and Caching

To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the mobile edge computing and caching framework has been widely regarded as a promising solution. When such a framework is implemented in heterogeneous cellular networks (HCNs), it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. In this article, under the resource partitioning and resource constraints, joint MD association and resource allocation is performed to minimize the total delay of MDs. In such a formulated problem, the MD association and resource allocation parameters are in a coupling form. In addition, it is also in nonlinear mixed-integer one and hard to tackle. To solve it, we try to develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. As for the designed algorithm, we provide some detailed convergence, stability, parallel implement, and computation complexity analyses. At last, in the simulation, we introduce other association algorithms for comparison, and investigate the impacts of MD density, time, and frequency partitioning factors on the delay of MDs. Simulation results show that the designed algorithm can achieve lower network delay than existing algorithms in general.