Search

The integration of distributed energy resources (DERs) into the power grid has made it important for distribution systems to participate in frequency regulation. Regulatory authorities (e.g., Federal Electricity Regulatory Commission in the United States) are recommending that DERs participate in energy and reserve markets, and a mechanism is needed to facilitate this at the distribution level. Though a single distribution system may not have sufficient reserves for tertiary frequency regulation, stacked reserves from multiple distribution systems can be utilized in frequency regulation. This paper proposes a coalitional game theory-based approach for reserve allocation, enabling DERs to participate in tertiary frequency regulation. The proposed two-stage approach involves computing worthiness index (WI) and power loss reduction (PLR) characteristic functions in the first stage and equivalent Shapley values in the second stage. The Shapley values are then used to determine distribution factors for reserve allocation among DERs. We demonstrate the effectiveness of the proposed method through case studies on IEEE 13-node, IEEE 34-node, and IEEE 123-node distribution test systems. The method is shown to be effective in allocating reserves and determining active power set-points of DERs, and can enable efficient participation of DERs in tertiary frequency regulation.

• Review of existing feature selection methods for Arabic topic detection. • Introduce a novel Hedonic Coalition Formation Game-based feature selection approach. • Use the proposed approach to provide inputs for an Arabic topic detection system. • Performance comparison against other well-known feature selection method. Arabic topic detection is a Natural Language Processing (NLP) task that aims to assign a topic or a set of topics to a new document based on its content and selected features. Feature selection is an essential topic detection technique used to capture discriminative and descriptive features in documents to enhance the performance of topic detection. This paper proposes a novel feature selection approach formulated as a Hedonic Coalition Formation Game. We use the proposed method to describe each topic with a unique Topic Oriented Vocabulary (TOV) vector. We inspect the performances of the vocabularies obtained by solving the Hedonic game in topic detection over several Arabic corpora. In addition, the proposed feature selection method is compared with a Mutual Information (MI) based vocabulary vector generation method. The reported results show the effectiveness of the proposed feature selection approach by generating new vocabularies with highly discriminating features and reduced dimensions. [ABSTRACT FROM AUTHOR]

The learning process takes place inside clients in federated learning (FL). How to effectively motivate clients and avoid the impact of statistical heterogeneity are challenges in FL. This article proposes contribution- and participation-based federated learning (CPFL) to address these challenges. CPFL can effectively allocate client incentives and aggregate models according to client contribution ratios, by which it can reduce the impact of heterogeneous data. To get effective and approximately fair client contributions faster, we propose an extended Raiffa solution (ERS). Compared to the conventional solution Shapley value, the time complexity of ERS goes from $\mathscr {O}(2^{n})$O(2n) down to $\mathscr {O}(n)$O(n). We perform extensive experiments with the MNIST/EMNIST datasets, heterogeneous datasets, and with different ratios of participation reward. Experimental results demonstrate that CPFL generally has a better learning effect in the heterogeneous case. [ABSTRACT FROM AUTHOR]

This paper explores the aggregation of electricity consumers flexibility. A novel coalitional game theory model for partition function games with non-transferable utility is proposed. This model is used to formalize a game in which electricity consumers find coalitions among themselves in order to trade their consumption flexibility in the electricity market. Utility functions are defined to enable measuring the players preferences. Two case studies are presented, including a simple illustrative case, which assesses and explains the model in detail; and a large-scale scenario based on real data, comprising more than 20,000 consumers. Results show that the proposed model is able to reach solutions that are more suitable for the consumers when compared to the solutions achieved by traditional aggregation techniques in power and energy systems, such as clustering-based methodologies. The solutions found by the proposed model consider the perspectives from all players involved in the game and thus are able to reflect the rational behaviour of the involved players, rather than imposing an aggregation solution that is only beneficial from the perspective of the aggregator.

This paper presents a coalitional game for value sharing in energy communities (ECs). It is proved that the game is super-additive, and the grand coalition effectively increases the global payoff. It is also proved that the model is balanced and thus, it has a nonempty core. This means there always exists at least one value sharing mechanism that makes the grand coalition stable. Therefore, prosumers will always achieve lower bills if they join to form larger ECs. A counterexample is presented to demonstrate that the game is not convex and value sharing based on Shapley values does not necessarily ensure the stability of the coalition. To find a stabilizing value sharing mechanism that belongs to the core of the game, the worst-case excess minimization concept is applied. In this concept, however, size of the optimization problem increases exponentially with respect to the number of members in EC. To make the problem computationally tractable, the idea of clustering members based on their generation/load profiles and considering the same profile and share for members in the same cluster is proposed here. K-means algorithm is used for clustering prosumers’ profiles. This way, the problem would have several redundant constraints that can be removed. The redundant constraints are identified and removed via the generalized Llewellyn’s rules. Finally, value sharing in an apartment building in the southern part of Finland in the metropolitan area is studied to demonstrate effectiveness of the method.

The trend of decentralization of energy services has given rise to community energy systems. These energy communities aim to maximize the self-consumption of local renewable energy generated and stored in assets that are typically connected to low-voltage (LV) distribution networks. Energy community schemes often involve jointly owned assets such as community-owned solar photo-voltaic panels (PVs), wind turbines and/or shared battery storage. This raises the question of how these assets should be controlled in real-time, and how the energy outputs from these jointly owned assets should be shared fairly among heterogeneous community members. Crucially, such real-time control and fair sharing of energy must also consider the technical constraints of the community, such as the local LV network characteristics, voltage limits and power ratings of electric cables and transformers. In this paper, we design and analyze a heuristic-based battery control algorithm that considers the influence of battery life degradation, and the resultant increase in local renewable energy consumption within local operating constraints of the LV network. We provide a model that first studies the techno-economic benefits of community-owned versus individually-owned energy assets considering the network/grid constraints. Then, using the methodology and principles from cooperative game theory, we propose a redistribution model for benefits in a community based on the marginal contribution of each household. The results from our study demonstrate that the redistribution mechanism is fairer and computationally tractable compared to the existing state-of-the-art methods. Thus, our methodology is more scalable with respect to modeling the economic sharing of joint assets in community energy systems.

Abstract Background Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance–the individual gene’s synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene’s contribution to the overall connectivity of its corresponding node in a biological network. Results We implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1–have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex. Conclusions Game theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.

This paper investigates the cooperative data exchange (CDE) scheme using the instantly decodable network coding across energy-constrained devices over the wireless channels. In fact, enabling the CDE brings several challenges, such as how to extend the network lifetime and how to reduce the number of transmissions in order to satisfy the urgent delay requirements. The problem is modeled using the cooperative game theory in partition form. Unlike most existing studies, which are only delay-sensitive, we take into account both the completion time and the consumed energy. We propose a distributed merge-and-split algorithm to allow the wireless nodes to self-organize into the independent disjoint coalitions in a distributed manner. Indeed, the proposed algorithm guarantees reduced energy consumption and minimizes the delay in the resulting clustered network structure. Note that we have considered not only the transmission energy but also the computational energy consumption. Moreover, we focus on the mobility issue and we analyze how, in the proposed framework, the nodes can adapt to the dynamics of the network. Such an important result offers insights into how to design the scalable energy and delay aware CDE framework. The simulation results validate the proposed framework and show that, interestingly, the nodes reduce both the energy consumption and the completion time.

Recently, visible light communication (VLC) networks have emerged as a promising alternative for indoor data access, due to high data rate, low implementation cost, and immunity to radio frequency (RF) interference. However, the co-existence of VLC with the RF access points as well as the dependence of VLC to room illumination compel both technologies to work in parallel and thus, to form a hybrid heterogeneous VLC/RF network. This network offers the advantages of both technologies, namely increased capacity and ubiquitous coverage. Furthermore, non-orthogonal multiple access (NOMA) is a very promising candidate technique for the next generation of wireless networks, mainly due to its increased spectrum efficiency compared to orthogonal access schemes. However, the optimal user grouping in NOMA is a combinatorial NP-complete problem, which calls for low complexity techniques. To this end, in this paper, we propose the use of coalitional game theory, where the users served by the same access point (VLC or RF) form a single coalition, while the users can switch through coalitions based on their payoff. A novel utility function is proposed that takes into account the peculiarities of the NOMA hybrid VLC/RF network. Finally, a coalition formation algorithm is presented as well as an efficient power allocation policy. Computer simulations validate the presented analysis and reveal the effectiveness of the proposed user grouping scheme compared to an opportunistic approach.

In radar networks, research on the task allocation strategy for multi-target imaging is essential, especially under limited radar resources. From the targets’ perspective, the goal is to acquire enough resources to obtain high-quality inverse synthetic aperture radar (ISAR) images. Then there is competition among targets for radar resources. Therefore, the task allocation optimization problem is considered as a cooperative game, specifically a non-transferable coalitional game when the sparse ISAR imaging algorithm is adopted. A stable coalition structure, which allows cooperation among targets to achieve satisfactory image resolution using the least amount of time, represents an optimal task allocation scheme. Then, the corresponding algorithm based on coalition-forming rules is proposed. Through the framework of cooperative game theory, a stable network structure (i.e., the task allocation strategy) is formed while minimizing the radar time resource cost. Finally, experiments confirm that the proposed algorithm can achieve an optimal task allocation strategy with a lower computation time cost. [ABSTRACT FROM AUTHOR]

Background: Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance–the individual gene's synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene's contribution to the overall connectivity of its corresponding node in a biological network. Results: We implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1–have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex. Conclusions: Game theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism. [ABSTRACT FROM AUTHOR]

Due to its theoretical virtues, several recent works propose the use of the incentive-compatible Vickrey–Clarke–Groves (VCG) mechanism for electricity markets. Coalitions of participants, however, can influence the VCG outcome to obtain higher collective utility. To address this issue, core-selecting mechanisms are proposed for their coalition-proofness. We show that core-selecting mechanisms generalize the economic rationale of the locational marginal pricing (LMP) mechanism. Namely, these mechanisms are the exact class of mechanisms that ensure the existence of a competitive equilibrium in linear/nonlinear prices. This implies that the LMP mechanism is also core-selecting, and hence coalition-proof. In contrast to the LMP mechanism, core-selecting mechanisms exist for a broad class of electricity markets, such as ones involving nonconvex costs and nonconvex constraint sets. In addition, they can approximate truthfulness without the price-taking assumption of the LMP mechanism. Finally, we show that they are also budget-balanced. Our results are verified with case studies based on OPF test systems and Swiss reserve market. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) and nonorthogonal multiple access (NOMA) are two enabling technologies in the 5 G network and beyond. MEC admits user equipments (UEs) running many more compute-intensive applications by providing computing capabilities at the network edge and within radio access networks, while NOMA enables multiple UEs to share the same resource block, thus leveraging considerable advantages such as greater spectral efficiency and a larger number of supported UEs. The state-of-the-art showed that the combination of NOMA and MEC can lower the energy consumption and/or overall latency; however, they mostly focused on single-carrier NOMA. In this paper, we investigate the computation offloading problem in multi-carrier NOMA enabled MEC systems and solve it from the cooperative game theory viewpoint using coalition formation game. Particularly, UEs are considered as game players and subcarriers are regarded as coalitions that can be used for computation offloading of multiple UEs. Based on the introduced coalition formation game, we develop a low-complexity algorithm with convergence guarantee to achieve the Nash-stable solution. Numerical results are provided to validate the effectiveness of the proposed coalition game based algorithm as well as its comparison with three baseline schemes. [ABSTRACT FROM AUTHOR]

In this work, we propose a distributed cooperative spectrum sensing (CSS) scheme for cognitive radio networks applying a greedy-based coalitional game. We formulate the cooperative spectrum sensing problem as a multi-objective optimization problem using the weighted global criteria method. The objective of the formulated problem is to jointly consider the relative impacts of the three key performance metrics in CSS: throughput gain, penalty cost, and energy overhead. By integrating these three distinct metrics into a global optimization problem, we aimed to achieve a well-balanced trade-off, essential for deploying the CSS scheme across diverse network and application environments. We prove that the formulated CSS problem is non-deterministic polynomially hard (NP-hard). The computational complexity of the formulated CSS problem is decreased by proposing a heuristic scheme that produces a sub-optimal solution in polynomial time. We develop the heuristic scheme by putting forward a greedy-based coalitional game theoretic model. The proposed scheme exploits the spatial diversity of secondary users (SUs) in forming distributed coalitions to minimize the effects of attenuation and distortion on the transmitted signal. Further sensing energy overhead is reduced by dynamically deciding the sensing duration for the SUs based on their received signal strength. To establish the stability of the coalitional game model, we carry out proof which shows that the proposed heuristic algorithm converges to a stable coalition structure. Simulation-based results show the effectiveness of the proposed scheme in terms of average detection accuracy, utility, and energy overhead compared to conventional schemes. [ABSTRACT FROM AUTHOR]

As the development of the energy industry, the integration and complementary of various energy resources will be trends of future multiple energy systems. Energy hub (EH) plays an important role in allocating energy resources efficiently. This study proposes a robust coalitional game theoretic optimisation model to execute the cooperative operation of multiple EHs with correlated wind power. First, an EH model with wind power generation is established. It considers both uncertainties and correlations of wind power. Then, coalitional game theory is introduced to solve the cooperation problem of multiple EHs. Distributed coalition formation algorithm called merge–split rule is developed to form coalitions of EHs. Furthermore, the robust optimisation method is presented to cope with the uncertainties of wind power. Scenarios of correlated historical data are covered by minimum volume enclosing ellipsoid and solved by Khachiyan's first‐order algorithm. Numerical results are given to verify the feasibility and efficiency of our proposed models and methods. [ABSTRACT FROM AUTHOR]

This paper considers secret-key generation between several agents and a base station that observe independent and identically distributed realizations of correlated random variables. Each agent wishes to generate the longest possible individual key with the base station by means of public communication. All keys must be jointly kept secret from all external entities. In this many-to-one secret-key generation setting, it can be shown that the agents can take advantage of a collective protocol to increase the sum rate of their generated keys. However, when each agent is only interested in maximizing its own secret-key rate, agents may be unwilling to participate in a collective protocol. Furthermore, when such a collective protocol is employed, how to fairly allocate individual key rates arises as a valid issue. This paper studies the tension between cooperation and self-interest with a game-theoretic treatment. This paper establishes that cooperation is in the best interest of all individualistic agents and that there exist individual secret-key rate allocations that incentivize the agents to follow the protocol. In addition, an explicit coding scheme that achieves such allocations is proposed. [ABSTRACT FROM AUTHOR]

In this paper, the problem of distributed mode selection, resource allocation, and interference management is studied for device-to-device (D2D) communications underlaying future cellular networks. In existing underlaid D2D communications, the base station (BS) typically assists D2D pairs in selecting a transmission mode to operate in (network-assisted), that is, either cellular, dedicated, or reuse mode. Network-assisted D2D communications assumes the BS has global knowledge of the network, which includes complete channel state information (CSI). However, acquiring global network information causes signaling overhead and network complexity to increase, due to the large number of users in the network. Therefore, we investigate user-assisted D2D communications as a viable solution for D2D communications, in which the BS has partial knowledge of the network (and partial CSI). To address this problem, a cross-layer coalitional (CLC) game is formulated, where D2D pairs and cellular users are clustered into coalitions based on resource allocation/sharing and mode selection, in a fully distributed and autonomous manner. Clustering D2D pairs allows resources to be shared fairly amongst users in a cluster, while also ensuring D2D pairs operate efficiently. To coordinate the interference between cellular and D2D pairs, an interference management subproblem is proposed that schedules D2D pairs reusing cellular user resources, as well as minimizing the transmission power of all users. To solve this game, a distributed algorithm is proposed, which converges to a Nash stable coalition partition that is socially efficient and has a nonempty core. Simulation results show that the proposed approach can effectively cluster D2D pairs into coalitions based on resource allocation and mode selection, which, in turn, reduces network sum power and increases network sum rate across all D2D pairs. [ABSTRACT FROM AUTHOR]

In order to avoid the environmental emission of fossil fuels, renewable energy resources expansion trend is undeniable. This paper analyzes the possibility of cooperation between neighboring independent microgids in a smart grid with renewable resources considering the security assets and vulnerabilities. The ultimate goal is to decrease the overall security risks on the smart grid. Modeling of microgrids cooperation is formulated via a coalition game definition considering the permissible amount of their vulnerabilities. Load shedding minimization can be obtained during cyber-attack occurrence for each member of coalition microgrids. This paper presents a comprehensive algorithm for optimal coalition of neighboring microgrids based on a utility function calculation which has three major components: Budget for Attack (BA), Budget for Defense (BD) and microgrid relationship effectiveness, to contribute more understanding, microgrid relationship effectiveness formulation is developed by the introduction of friction and influence matrices. Proportional Fairness (PF) index is employed to illustrate the improvement of utility function in case of optimal coalition in comparison with non-cooperative state. Microgrids coalition optimal choice based on suggested algorithm is validated by probability attack and loss of load probability (LOLP) simulation for each microgrid before and after the coalition form. [ABSTRACT FROM AUTHOR]

Research and Managerial Summary: We examine the effect of any new agent on the value captured by an incumbent: e.g., a new rival, a new source of supply, a new customer, etc. Regardless of the new agent's type, entry has two opposing effects on the incumbent's minimum profitability guaranteed by competition. First, is the creation of more economic value. This tends to blunt competition and lowers minimum guaranteed profit. Second, is the potential generation of new productive alternatives for the incumbent, thereby inducing the opposite effect. We describe whether entry allows guaranteed profits to persist or creates guaranteed profits when they did not previously exist. We show that knowing that the entrant can replace the incumbent is not enough to determine the impact of entry on guaranteed minimum profits. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel user cooperation scheme based on weighted fractional Fourier transform (WFRFT), to enhance the physical (PHY) layer security of wireless transmissions against eavesdropping. Specifically, instead of dissipating additional transmission power for friendly jamming, by leveraging the features of WFRFT, the information bearing signal of cooperators can create an identical artificial noise effect at the eavesdropper while causing no performance degradation on the legitimate receiver. Furthermore, to form the cooperation set in an autonomous and distributed manner, we model WFRFT-based PHY-layer security cooperation problem as a coalitional game with non-transferable utility. A distributed merge-and-split algorithm is devised to facilitate the autonomous coalition formation to maximize the security capacity while accounting for the cooperation cost in terms of power consumption. We analyze the stability of the proposed algorithm and also investigate how the network topology efficiently adapts to the mobility of intermediate nodes. Simulation results demonstrate that the WFRFT-based user cooperation scheme leads to a significant performance advantage, in terms of secrecy ergodic capacity, compared with the conventional security-oriented user cooperation schemes, such as relay-jamming and cluster-beamforming. [ABSTRACT FROM PUBLISHER]

The Internet consists of economically selfish players in terms of access/transit connection and content distribution. Such selfish behaviors often lead to techno-economic inefficiencies, such as unstable peering and revenue imbalance. Recent research results suggest that cooperation-based fair revenue sharing, i.e., multi-level Internet service provider (ISP) settlements, can be a candidate solution to avoid unfair revenue share. However, it has been under-explored whether selfish ISPs actually cooperate or not (often referred to as the stability of coalition), because they may partially cooperate or even do not cooperate, depending on how much revenue is distributed to each individual ISP. In this paper, we study this stability of coalition in the Internet, where our aim is to investigate the conditions under which ISPs cooperate under different regimes on the traffic demand and network bandwidth. We first consider the under-demanded regime, i.e., network bandwidth exceeds traffic demand, where revenue sharing based on Shapley value leads ISPs to entirely cooperate, i.e., stability of the grand coalition. Next, we consider the over-demanded regime, i.e., traffic demand exceeds network bandwidth, where there may exist some ISPs who deviate from the grand coalition. In particular, this deviation depends on how users’ traffic is handled inside the network, for which we consider three traffic scheduling policies having various degrees of content-value preference. We analytically compare those three scheduling policies in terms of network neutrality, and stability of cooperation that provides useful implications on when and how multi-level ISP settlements help and how the Internet should be operated for stable peering and revenue balance among ISPs. [ABSTRACT FROM AUTHOR]

Purpose: we prepared a questionnaire to evaluate Incompatible parts and also risk management in University of Science and Technology E-Learning Center and studying the Incompatible parts impacts on utility of organization. Design/methodology/approach: By using coalitional game theory we present a new model to recognize the degrees of incompatibility among independent divisions of an organization with dependent security assets. Based on positive and negative interdependencies in the parts, the model provides how the organization can decrease the security risks through non-cooperation rather than cooperation. we implement the proposed model of this paper by analyzing the data which have been provided by questionnaires from different three managers' ideas of Iran University of Science and Technology E-Learning Center located in Iran University of Science and Technology, Tehran, Iran. Findings: In general, by collecting data and analyzing them, the survey showed that Incompatible parts of organizations have negative impacts on utility of organization risk management process. Furthermore, it adds values to other organizations and provides the best practices in planning, developing, implementing and monitoring risk management in organizations. Research limitations/implications: Since Information security and also Risk Management are still areas which need to improve in some Iranian universities, we couldn't consider them in our analysis. On the other hand, due to questionnaire limitation, the study's sample size is 1. This size may be considered large for our statistical analysis. Originality/value: The main contribution of this paper is to propose a model for noncooperation among a number of divisions in an organization and using risk management factors. [ABSTRACT FROM AUTHOR]

This paper studies the strategic behavior of a large number of game designers and studies the scalability, stability and robustness of their allocations in a large number of homogeneous coalitional games with transferable utilities (TU). For each TU game, the characteristic function is a continuous-time stochastic process. In each game, a game designer allocates revenues based on the extra reward that a coalition has received up to the current time and the extra reward that the same coalition has received in the other games. The approach is based on the theory of mean-field games with heterogeneous groups in a multi-population regime. [ABSTRACT FROM AUTHOR]

The liberalization of wireless communication services markets and the subsequent competition among network operators, is expected to foster optimal utilization of the scarce wireless spectrum and ensure the provision of cost-efficient services to users. However, such markets may function inefficiently due to collusion of operators which yields a de-facto monopoly. Although it is illegal and detrimental to the users, creation of such cartels arises often in the form of implicit price fixing. In this paper, we consider a general such market where a set of operators sell communication services to a large population of users. We use an evolutionary game model to capture the user dynamics in selecting operators, under limited information about the actual service quality, and we analyze the anticipated interaction of the operators using coalitional game theory. We define a coalition formation game in order to rigorously study the conditions that render monopolistic or oligopolistic markets stable under different notions of coalition stability. We also provide direct and indirect regulation methods, such as setting price upper bounds or allocating different amounts of spectrum, in order to discourage undesirable equilibriums. Our approach provides intuitions about collusion strategies, as well as on directions for identifying and preventing them. [ABSTRACT FROM PUBLISHER]

We consider the uplink of a cellular massive multiple-input multiple-output network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is non-trivial, especially in practical asymmetric BS deployments. We approach this problem using the coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition, thus, benefit from serving more users in their cells at the expense of higher pilot contamination and interference. Given that a cell’s average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in the partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a BS intercommunication budget constraint, we are able to control the overhead in message exchange between the BSs and ensure the algorithm’s convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern. [ABSTRACT FROM PUBLISHER]

This paper presents an optimal coalition formation mechanism of microgrids in a smart distribution system and analyzes the characteristics from the coalitional game theoretical perspective. Microgrids coalitions can (1) minimize the energy burden and dependency on the utility grid, (2) minimize the overall grid network power loss, and (3) maximize intra-coalition energy transfer. In order to form cooperative microgrids, a Hierarchical priority based Coalition Scheme ( HRCoalition ) is proposed. Given an intra-coalition distance threshold, the proposed HRCoalition mechanism can provide the optimal coalition that achieves the aforementioned objectives. The optimality is realized by reaching a state of cooperative equilibrium for all microgrids and coalitions. The optimality of the formed coalitions is proved by Coalitional Game Theory . A Greedy based strategy is designed to perform network constrained energy exchange (GreedEnEx) within a formed coalition. Thus, HRCoalition provides a higher level optimization while, GreedEnEx yields system level optimization using output of HRCoalition . The proposed HRCoalition scheme is computationally very efficient and can scale up to a huge number of microgrids and thus makes it suitable for near real-time operation. An equivalent pricing mechanism is designed to provide a form of economic incentive to the microgrids participating coalition formation. The performance of the proposed method is reported to scale up to 500 microgrids with a loss reduction ranging from 26% to 80%. The provided numerical simulation results back the claim of optimality as well as prove the effectiveness of the proposed coalition formation method. [ABSTRACT FROM AUTHOR]

Femtocell networks have the potential to satisfy the increasing demand of mobile data usage. The recently proposed concept of cognitive femtocell network provides an effective way to further improve the spectrum spatial and frequency reuse. In this paper, we study the subchannel allocation problem for orthogonal frequency division multiple access (OFDMA)-based hybrid overlay/underlay cognitive femtocell networks. While most of the previous related studies did not fully exploit the potential of spatial and frequency reuse of the network, we propose a hybrid overlay and underlay spectrum access mechanism to further improve the performance of cognitive femtocell networks. We formulate the subchannel allocation problem as a coalition formation game among femtocell users under the hybrid access scheme, and analyze the stability of the coalition structure. We propose an efficient algorithm based on the solution concept of recursive core, and achieve a stable and efficient allocation. Simulation results show that the proposed algorithm achieves an improvement in aggregate network throughput up to 72% comparing to the overlay only scheme, 35% comparing to the underlay only scheme, and 18% comparing to a recently proposed coalition formation algorithm in the literature. [ABSTRACT FROM PUBLISHER]

The purpose of this contribution is to set up a language to evaluate the results of concerted action among interdependent agents against predetermined properties that we can recognize as desirable from a deontic point of view. Unlike the standard view of logics to reason about coalitionally rational action, the capacity of a set of agents to take a rational decision will be restricted to what we will call agreements, which can be seen as solution concepts to a dependence structure present in a certain game. The language will identify those agreements that act accordingly or disaccordingly with the desirable properties arbitrarily set up in the beginning, and will reveal, by logical reasoning, a variety of structural properties of this type of collective action. [ABSTRACT FROM AUTHOR]

To combat erasure channels, multiple description coding (MDC) introduces diversity at source coding level. Each description provides a coarse reconstruction of source information at destination. As the number of received description increases, the reconstruction quality would be improved. Rate region of MDC is the set of achievable rates for a given set of expected distortions. In this paper, coalitional game theory is proposed as a nontrivial approach for determining rate region of MDC. To this end, elaborate game theoretic analysis is provided. Moreover, Shapley value is utilized to determine each description's rate. As a result, a fair rate adjustment is obtained that comprises entropy and mutual information of original and reconstructed descriptions. [ABSTRACT FROM PUBLISHER]

This letter investigates resource allocation for orthogonal frequency division multiple access (OFDMA)-based multicast cognitive radio networks (MCRNs) under the consideration of primary users' activities. Due to the heterogeneity of channel gains among secondary users (SUs), the existing multicast schemes are highly conservative and spectrally inefficient. To address this issue, we formulate a novel multicasting problem for MCRNs. This new formulation optimally and adaptively exploits multiuser diversity of OFDMA through dynamic group formation, which clusters SUs within a multicast group into multiple smaller subgroups (coalitions) based on their channel gains. Subcarriers and power are then allocated to these subgroups to maximize aggregate data rate (ADR) of the system. A coalitional game theory is adopted to model the group formation where SUs are allowed to form coalitions to compete for resources. A novel cognitive multicast coalitional game (CMCG) is proposed for SUs to self-organize to reach multi-coalitional equilibrium where optimality can be obtained. Simulation results show that the proposed CMCG algorithm significantly improves the system ADR as compared to the conventional unicast and multicast schemes. [ABSTRACT FROM AUTHOR]

Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU's return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs. [ABSTRACT FROM AUTHOR]

Executing large-scale application programs in grids requires resources from several grid service providers (GSPs). These providers form virtual organizations (VOs) by pooling their resources together to provide the required capabilities to execute the application. We model the VO formation in grids using concepts from the coalitional game theory and design a mechanism for VO formation. The mechanism enables the GSPs to organize into VOs reducing the cost of execution and guaranteeing maximum profit for the GSPs. Furthermore, the mechanism guarantees that the VOs are stable, that is, the GSPs do not have incentives to break away from the current VO and join some other VO. We perform extensive simulation experiments using real-workload traces to characterize the properties of the proposed mechanism. The results show that the mechanism produces VOs that are stable yielding high revenue for the participating GSPs. [ABSTRACT FROM AUTHOR]

The performance of cooperation strategies for broadcast services sharing a common wireless channel is studied in the framework of coalitional game theory. Two strategies are examined. The first represents an open sharing model where each service provider is allowed to transmit at any time but simultaneous transmissions result in interference. It is shown analytically that in the absence of coordination cost, the grand coalition formed by all providers cooperating to avoid simultaneous transmissions is both sum-rate optimal and stable. The second strategy represents an orthogonal access method where service providers are granted exclusive access to a subset of the available channels, each having a guaranteed successful transmission opportunity. In the absence of coordination cost, the grand coalition where all providers cooperate by sharing their guaranteed right to access the channel is sum-rate optimal but unstable, in the sense that some group of providers may have an incentive to deviate from the grand coalition. In the presence of coordination cost, a different scenario arises. In both models large coalitions do not form, and simulation results suggest that the open access model for large networks can lead to a regime where performance is considerably limited by interference. [ABSTRACT FROM PUBLISHER]

Abstract: We consider a simple model of cooperation among agents called Coalitional Skill Games (CSGs). This is a restricted form of coalitional games, where each agent has a set of skills that are required to complete various tasks. Each task requires a set of skills in order to be completed, and a coalition can accomplish the task only if the coalitionʼs agents cover the set of required skills for the task. The gain for a coalition depends only on the subset of tasks it can complete. We consider the computational complexity of several problems in CSGs, such as testing if an agent is a dummy or veto agent, computing the core and core-related solution concepts, and computing power indices such as the Shapley value and Banzhaf power index. [Copyright &y& Elsevier]

A coalitional game theoretic scheme is proposed that aims at maximizing wireless sensor network lifetime under specified QoS. Employing a small number of nodes of increased computing power and lifetime called representatives, an adaptive clustering scheme is proposed where neighboring nodes form coalitions in order to increase energy efficiency at the cost of controllable data-accuracy reduction. The coalition formation is globally optimized by the representatives. The spatial correlation of the sensed phenomenon measurements is exploited to formulate a cooperation scheme that reduces drastically the number of node transmissions. The specifications regarding the accuracy of the collected data determine the extent of coalition formation. The efficiency and stability of the proposed coalitional scheme are studied through simulations. [ABSTRACT FROM AUTHOR]

Web services (WSs) can cooperate with each other to provide more valuable WSs. Current approaches for WS cooperation have typically assumed that WSs are always willing to participate in some form of cooperation, and have undermined the fact that WSs are autonomous in this open environment. This assumption, however, becomes more problematic in community-based WS cooperation due to the dynamic nature of WS community. It is, therefore, important to devise a cooperation scheme respecting WS autonomy for community-based WS cooperation. In this paper, we model the community-based cooperation among autonomous WSs as a coalitional game in graph form. We show this game is non-cohesive and design a distributed coalition formation algorithm. We prove that the proposed algorithm can lead to an individually stable coalition partition, which indicates that every WS can maximize its benefit through cooperation without decreasing other WSs' benefit. We also conduct extensive simulations, and the results show that the proposed algorithm can greatly improve the average payoff per WS and average availability per coalition when compared with other cooperation schemes. [ABSTRACT FROM PUBLISHER]

In a great number of applications, it is necessary to distribute resources or tasks to agents collaborating with each other in order to maximize the social welfare of the structure they belong to. The question in these cases is how to divide in a fair way the outcome that the structure eventually earns, say money, to the participating agents. The paper faces this issue by focusing on a real-world application: the distribution of funds to Italian research structures and substructures, after a research assessment program (known as VQR) that is currently evaluating the Italian research production over years 2004-2010. A number of desirable properties for any reasonable fair division rule are identified and exemplified for the case-study application. For instance, it is argued that the money distribution should be independent of possible alternative allocations of research products, and it should consider the actual contribution of every researcher to the outcome of the structure (s)he belongs to. Moreover, the whole process starting from the preliminary phase of products selection is dealt with, in order to prevent possible strategic behaviors from researchers that may lead their structure to miss its best possible score in the evaluation process. A fair solution based on the notion of Shapley value is described and analyzed. It turns out that the proposed solution enjoys all the desirable properties of fair division rules, and it could effectively be implemented in the VQR, as well as in related division-rules applications. [ABSTRACT FROM AUTHOR]

Cooperative spectrum sensing is an effective technique to improve the sensing performance and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we consider a CRN with multiple primary users (PUs) and multiple secondary users (SUs). We first propose a cooperative spectrum sensing and access (CSSA) scheme for all the SUs, where the SUs cooperatively sense the licensed channels of the PUs in the sensing subframe. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission subframe. We then formulate this multi-channel spectrum sensing and channel access problem as a hedonic coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The value function of each coalition and the utility function of each SU take into account both the sensing accuracy and the energy consumption. We propose an algorithm for decision node selection in a coalition. Moreover, we propose an algorithm based on the switch rule to allow the SUs to make decisions on whether to join or leave a coalition. We prove analytically that the set with all the SUs converges to a final network partition, which is both Nash-stable and individually stable. Besides, the proposed algorithms are adaptive to changes in network conditions. Simulation results show that our proposed CSSA scheme achieves a better performance than the closest PU (CPU) scheme and the noncooperative spectrum sensing and access (NSSA) scheme in terms of the average utility of the SUs. [ABSTRACT FROM PUBLISHER]

Abstract: k-Anonymity is one of the most popular techniques for protecting the privacy of an individual and hence preventing an adversary from gaining sensitive information. The observed limitations in this process are: i) The anonymization is done based on an assumed value of k, ii) the information loss can be found only after the anonymization is done, iii) if the information loss is found to be more than the affordable loss then another k is assumed, iv) For each k the whole process has to be repeated. This paper discusses a novel approach using Coalitional Game Theory to overcome the limitations of k-anonymity. The approach helps fix up the privacy levels based on the threshold given for the information loss. To achieve the anonymity, each tuple is assumed to be an agent and is assigned payoff using concept hierarchy tree (CHT) of quasi identifiers (QID) of the given data, later coalitions between the tuples are formed based on their payoffs. We can fix up the boundaries of k for k-anonymity based on the threshold given for the information loss. The experimental results showing the practicality and scalability are presented. [Copyright &y& Elsevier]

This paper considers the problem of resource allocation for a large-scale wireless network consisting of multiple amplify-and-forward (AF) cooperative links. To effectively manage interlink interference and achieve efficient resource allocation, we propose a novel two-stage game model in which links form coalitions in the first stage, and in the second stage, they engage in a noncooperative game given the coalition structure determined in the first stage. This model is distinct from the classic coalitional game theory as it captures the important possibilities of externalities across coalitions. In the coalitional game formulation, we have derived an analytical expression for the asymptotic throughput of an AF cooperative system in the regime of large number of relays and have incorporated opportunistic relaying as an effective technique to combat harmful interferences. In the sequential noncooperative game formulation, the uniqueness of the Nash equilibrium has been established for the general N-player case, which generalizes previous works on the two-player game. Simulation results demonstrate over 200% sum rate enhancement relative to the noncooperative approach, primarily due to the improved pattern of frequency reuse and the benefit of multiuser diversity. [ABSTRACT FROM PUBLISHER]