Overlapping Coalition Formation Research Articles

A Heuristic Task Allocation Method Based on Overlapping Coalition Formation Game for Heterogeneous UAVs

A Heuristic Task Allocation Method Based on Overlapping Coalition Formation Game for Heterogeneous UAVs

TBOMC: A Task-Block-Based Overlapping Matching-Coalition Scheme for Task Offloading in Vehicular Fog Computing

Vehicular fog computing (VFC) is regarded as a promising framework for vehicular computing applications by utilizing local spare resources of nearby vehicles to conduct ubiquitous time-critical and data-intensive tasks. Meanwhile, how to provide stable and low-latency services through real-time task offloading has become a heated issue. Opposite to the traditional task-to-individual offloading manner, in this paper, we propose a novel task-block (TB)-based offloading paradigm for VFC, in which the tasks are merged into blocks to be assigned and offloaded. This TB-based offloading paradigm effectively alleviates the offloading decision-making burden and thus reduces the overall computation latency in the dynamic vehicular environment. Faced with transmission-reliable and time-intensive requirements of TBs, we turn to cooperation among vehicles and further propose a TB-based Overlapping Matching-Coalition (TBOMC) scheme integrating overlapping coalition formation (OCF) game with matching theory to address the complicated offloading problem. The OCF game framework encourages vehicular fog nodes to devote their resources and form collaborative computing groups in a distributed method. Numerical results demonstrate that the TBOMC scheme better exploits local computing capabilities and outperforms from 5% to 12% over other existing benchmarks.

Dynamic Overlapping Coalition Formation in Electricity Markets: An Extended Formal Model

The future power system will be characterized by many small decentralized power plants—so-called distributed energy resources (DERs). The integration of these DERs is vital from an economic and grid operation point of view. One approach to this is the aggregation of such DERs. The formation of coalitions as an aggregation method has already been examined in the literature and applied in virtual power plants, active distribution networks, and microgrids. The spread of DERs also increases the need for flexibility and dynamics in the power grid. One approach to address this can be overlapping coalitions. Therefore, in this paper, we first performed an analysis of related work and, in this context, found no work on overlapping coalitions for energy use cases in the literature. We then described a method for dynamic coalition formation, called dynamic coalition in electricity markets (DYCE), and analyzed how DYCE would need to be extended to include overlapping coalition formation. The extension includes the phases of product portfolio optimization and the actual coalition formation. Our analysis of DYCE shows that the methods used for the optimization of the DYCE sub-tasks are not suitable for overlapping coalitions and would have to be replaced by other methods in order to be able to form overlapping coalitions.

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

A Task-Driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

Federated HD Map Updating Through Overlapping Coalition Formation Game

High Definition (HD) maps have become core supporting components for autonomous driving. To date, their updates heavily depend on the vehicle fleets of the map vendors, which cannot scale and timely reflect the highly dynamic environment. To ensure the HD map quality, it is advocated social vehicles should be used. Nevertheless, there are privacy concerns and a lack of incentives for social vehicles to contribute data. In this paper, we leverage federated analytics (FA), a newly developed collaborative data analytics paradigm, where raw data are kept local and only the insights generated from local analytics are sent to a server for aggregation. We present a new Federated Analytics based HD map Updating model (FAUMap) to protect the privacy of social vehicles. To motivate social vehicles to contribute data and improve the HD map quality, we formulate an overlapping coalition formation game, OCFUMap, and develop an algorithm to find feasible coalitions. Simulations show that our approach can improve the quality of the updated HD map by 1.56 times. To study an end-to-end operation of the FAUMap model and OCFUMap game, we present a case of HD map updates of the Powell street in San Francisco using the autonomous driving simulator CarLA.

OCVC: An Overlapping-Enabled Cooperative Vehicular Fog Computing Protocol

With increasing time-critical and computation-intensive tasks generated by mobile applications, vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on roadside units (RSUs) or cloud centers. In VFC, tasks are offloaded to vehicular fog nodes local to the client devices, which exploits the under-explored computational resources of nearby vehicles. In this paper, we propose a novel cooperative vehicular fog computing architecture from an overlapping perspective, termed Overlapping-enabled Cooperative Vehicular fog Computing (OCVC) to fully utilize vehicular fog nodes' local potential resources. Different from traditional cooperative VFC architecture where each vehicle only works in one fog computing group at one time, the proposed OCVC architecture enables vehicles to participate in different computing groups simultaneously, and thus is able to fully exploit potential computational resources in an overlapping manner. In addition, we provide a distributed OCVC scheme to solve the complicated computing group formation, overlapping resource allocation, and task assignment problem by employing the overlapping coalition formation (OCF) game framework and a heuristic offloading algorithm. We conduct simulations for performance comparison in terms of diversified performance metrics and numerical results show that the proposed OCVC scheme performs better than other benchmarks under different conditions.

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.

An EKF based overlapping coalition formation game for cooperative wireless network navigation

The problem of efficient link selections for cooperative navigation is investigated in dynamic wireless networks, using an extended Kalman filter based overlapping coalition formation (OCF) game approach. Due to the densely deployment of nodes without proper link selections, the conventional cooperative navigation approaches usually yield high time-frequency resources as the existing resource management methods only work properly in static localisation systems. A new link selection mechanism which is reliable with limited resources in dynamic positioning scenarios is proposed. The varying topological structure and states of every node will be taken into consideration while each node abandons a considerable number of unnecessary links. Firstly, the state of every mobile node will be estimated in real-time. Then a selection criterion is applied to determine whether nodes are well navigated. If the criterion is satisfied, an OCF game will be employed to construct the effective link selection. Otherwise, a re-link mechanism will be used to obtain precise state information of each node. Numerical results show that the cooperative navigation algorithm can significantly improve the positioning accuracy and the proposed OCF game approach can build a cooperative network with limited number of links at the cost of appropriate performance degradation.

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.

Cooperative game approach to form overlapping cloud federation based on inter-cloud architecture

In order to provide uninterrupted services and fulfill the requirements of customers, Horizontal Cloud Federation Formation (HCFF) has been proposed as a new model enabling cloud providers to cooperate and enlarge their virtual machine infrastructure capacity. In this paper, we have established a synthesis on the main related works in the literature by classifying them into two classes: reactive protocols of HCFF and proactive protocols of HCFF, while doing a comparison between them according to a number of well chosen criteria. Furthermore, we have proposed three protocols of cloud federation formation based on the theory of cooperative games. The protocols are: Sequential Protocol of Horizontal Cloud Federation Formation (SPHCFF), Parallel PHCFF (PPHCFF), and Overlapping PHCFF (OPHCFF). More precisely, our main contribution in this research is the introduction of the notion “Overlapping Federations” with the OPHCFF protocol by referring to the class of Overlapping Coalition Formation Games. Moreover, we have developed a new system of three components based on Inter-Cloud architecture, and then we have implemented it in real time to evaluate the proposed protocols. The obtained experimental results have shown that the OPHCFF protocol improves the average response time, the rate of processed applications, and the profit compared with the introduced PPHCFF protocol. In addition, despite the dissimilarities of architectures, protocols, and mathematical models of existing works, we have arrived to manage a simulation comparison of our SPHCFF with a concurrent mechanism called Cloud Federation Formation Mechanism (CFFM). The obtained simulation results have shown that the proposed SPHCFF performs better than the CFFM mechanism, when the overload rate is large enough, in terms of profit, federation size, time execution, and failure rate.

Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN.

Cooperative multipoint transmission (CoMP) is one of the most promising paradigms for mitigating interference in cloud radio access networks (C-RAN). It allows multiple remote radio units (RRUs) to transmit the same data flow to a user to further improve the signal quality. However, CoMP may incur redundant data transmission over fronthaul network in the C-RAN. In a C-RAN employing CoMP, a key problem is how to coordinate heterogeneous resource allocation to maximize the cooperation gain while reducing the fronthaul load. In this paper, the cooperation transmission based on a multi-dimensional resource schedule (MRSCT) scheme, jointly considering user association, spectrum resource allocation, and wavelength resource allocation, is firstly envisioned in the underlying C-RAN integrating time and wavelength division multiplexing passive optical network (TWDM-PON) to maximize fronthaul efficiency. Then a two-timescale resource allocation framework including two sub-approaches is established. More specially, the first sub-approach mainly focuses on exploiting reinforcement learning to obtain a wavelength resource allocation strategy to relieve fronthaul traffic load. Moreover, the second sub-approach adopts the overlapping coalition formation game to establish a user-centric cooperative set, where spectrum resources are dynamically allocated to further alleviate the interference issue. The theoretical analysis and simulation results validate the performance of MRSCT scheme on the fronthaul efficiency, user experience, and system service capability.

A Two-Stage Game Framework to Secure Transmission in Two-Tier UAV Networks

The multi-UAV network is promising to extend conventional networks by providing broader coverage, and better reliability. Nevertheless, the broadcast nature of wireless signals, and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV, and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs), and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage, and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities, and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm, and an OCF algorithm are proposed to solve these two sequential games whose convergences, and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms, and the effectiveness of our two-stage game framework in the terms of secrecy performance.

Generalized User Grouping in NOMA Based on Overlapping Coalition Formation Game

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.

Efficient Spectrum Slicing in 5G Networks: An Overlapping Coalition Formation Approach

Mobile Network Operators perceive Spectrum Slicing and Sharing as an efficient and viable option to meet the growing mobile user data demands in the upcoming 5G network deployments. Efficient spectrum sharing requires optimized spectrum partitioning, i.e., slicing of the spectrum for sharing among operators, to improve spectrum utilization, offer services with high quality of experience, and increase their revenues. This paper proposes Spectrum Slicing Coalition Formation (SSCF) protocol, a novel solution to address effective spectrum slicing required to meet the dynamic data demands at every 5G base station. The base stations’ cooperation is formulated as a coalition formation game with overlapping coalitions , where a base station's contribution is not restricted to a single coalition, instead a base station participates intelligently in multiple coalitions. The SSCF protocol enables a base station to dynamically slice and share its allocated spectrum to service its load based spectral requirements, a key requirement for effective radio access network softwarization. The study explores operator base stations’ options to determine with whom, when, and how much to slice and share the spectrum and the payoffs. Extensive simulations using real-world operator base station deployments highlight that SSCF solution brings in operational efficiencies, improved users’ quality of experience, and revenue maximization.

An Overlapping Coalition Formation Game Based Multicast Scheme in Backhaul-Limited Small Cell Networks

This paper studies the resource allocation for multicast transmission in wireless small cell networks, which allocates appropriate Modulation and Coding Scheme (MCS) on each physical Resource Block (RB). This problem is not trivial due to the impact of interference and the limited wireless backhaul. We first formulate our concerned problem with the objective of maximizing the network throughput. Afterwards, we utilize framed overlapping coalition formulation game theory to design a distributed resource allocation algorithm. We further analyze the stability and convergence of the proposed algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is an important factor when identifying the optimal multicast resource allocation solution, and our proposed algorithm can significantly improve the multicast throughput.

A Task-Oriented Heuristic for Repairing Infeasible Solutions to Overlapping Coalition Structure Generation

Overlapping coalition formation (OCF), which provides a natural framework for modeling scenarios where each agent can join and allocate their resources to several completely different coalitions at the same time, has become a very active topic in multiagent systems. For OCF in resource-constrained and subadditive task oriented domains, an agent may not possess sufficient resources to meet the needs of multiple coalitions simultaneously. As a result, there may exist many potential resource conflicts among the rival overlapping coalitions. To tackle such situations, we first present a natural variation of the traditional OCF model and analyze the size of the solution space and the computational complexity of the overlapping coalition structure generation (OCSG) problem. Next, we develop a generic task-oriented heuristic (TOH) for individual repairs that can be used in binary meta-heuristic algorithms to generate overlapping coalitions in a parallel manner. Moreover, we show how the proposed TOH repairs a 2-D individual to resolve resource conflicts and discuss several basic properties. Finally, to evaluate the effectiveness of TOH, we compare it with the existing agent-oriented heuristic for the OCSG problem. The empirical results demonstrate that TOH is of high efficiency and effectiveness in harsh environments with fierce competition over scarce resources.

Coalition Game-Based Strategy for Resource Allocation and Transmit Power Control in D2D Communication

Resource allocation and co-channel interference are the two major technical challenges in the implementation of device-to-device communication technology. We propose an innovative resource allocation strategy along with dynamic power control for D2D communication. The resource allocation problem is formulated as a single utility function for multiple D2D pairs and cellular users in underlaying cellular networks. The proposed strategy based on overlapping coalition formation game allows a D2D pair to reuse multiple resource blocks. It aims to maximize the system sum rate maintaining minimal adverse impact of the interference on the overall system performance. Numerical results obtained through computer simulation show that the proposed strategy significantly improves the system sum rate along with reduced power consumption.

Cooperative games with overlapping coalitions: Charting the tractability frontier

The framework of cooperative games with overlapping coalitions (OCF games), which was proposed by Chalkiadakis et al. [1], generalizes classic cooperative games to settings where agents may belong to more than one coalition. OCF games can be used to model scenarios where agents distribute resources, such as time or energy, among several tasks, and then divide the payoffs generated by these tasks in a fair and/or stable manner. As the framework of OCF games is very expressive, identifying settings that admit efficient algorithms for computing ‘good’ outcomes of OCF games is a challenging task. In this work, we put forward two approaches that lead to tractability results for OCF games. First, we propose a discretized model of overlapping coalition formation, where each agent i has a weight Wi∈N and may allocate an integer amount of weight to any task. Within this framework, we focus on the computation of outcomes that are socially optimal and/or stable. We discover that the algorithmic complexity of this task crucially depends on the amount of resources that each agent possesses, the maximum coalition size, and the pattern of communication among the agents. We identify several constraints that lead to tractable subclasses of discrete OCF games, and supplement our tractability results by hardness proofs, which clarify the role of our constraints. Second, we introduce and analyze a natural class of (continuous) OCF games—the Linear Bottleneck Games. We show that such games always admit a stable outcome, even assuming a large space of feasible deviations, and provide an efficient algorithm for computing such outcomes.

Joint Interference Management in Ultra-Dense Small-Cell Networks: A Multi-Domain Coordination Perspective

Extensive deployment of heterogeneous small cells in cellular networks results in ultra-dense small-cell networks (USNs). The USNs have been established as one of the vital networking architectures in the 5G to expand system capacity and augment network coverage. However, intensive deployment of cells results in a complex interference problem. In this paper, we propose a distributed multi-domain interference management scheme among cooperative small cells. The proposed scheme mitigates the interference while optimizing the overall network utility. In addition, we jointly investigate OFDMA scheduling, TDMA scheduling, interference alignment (IA), and power control. We model small cells’ coordination behavior as an overlapping coalition formation game. In this game, each base station can make an autonomous decision and participate in more than one coalition to perform IA and suppress intra-coalition interference. To achieve this goal, we propose a distributed joint interference management (JIM) algorithm. The proposed algorithm allows each small-cell base station to self-organize and interact into a stable overlapping coalition structure and reduce interference gradually from multi-domain, thus achieving an optimal tradeoff between costs and benefits. Compared with existing approaches, the proposed JIM algorithm provides appreciable performance improvement in terms of total throughput, which is demonstrated by simulation results.