Coordinated coalition formation in throughput‐efficient cognitive radio networks

In this paper, we consider the problem of joint coalition formation (CF) and bandwidth allocation in cognitive radio networks. We assume that the secondary links will be partitioned into disjoint coalitions, by which the available spectrum will be reused. On the other hand, we assume that the members of each coalition will transmit over orthogonal sub‐bands with the available spectrum being optimally allocated among them. We formulate the problem as a coordinated CF game, where the optimization is held at a secondary coordinator that has all the required channel information. Given these assumptions, we derive a closed‐form expression of the optimal bandwidth allocation for any given network partition, and we propose an efficient algorithm to reach a Nash‐stable partition (coalition structure), with the objective of improving the network throughput. Furthermore, we provide a probabilistic analysis of the stability of a grand structure and a singleton structure, and we employ this to obtain a lower bound on the stability of a general coalition structure (other than grand and singleton). Performance analysis shows that the proposed CF algorithm with optimal bandwidth allocation provides a substantial gain in the network throughput over existing CF techniques as well as the simple cases of singleton and grand coalitions. Copyright © 2015 John Wiley & Sons, Ltd.     

A Relaying Incentive Scheme for Multihop Cellular Networks Based on Coalition Game with Externalities

Cooperative multihop communication can greatly increase network throughput, yet packet forwarding may involve opportunity and energy cost for relays. Thus one of the primary problems in the implementation of multihop transmission is how to foster cooperation among selfish nodes. Existing researches mainly adopt price and monetary stimulating. We propose instead a self-enforcing incentive scheme free of monetary remunerating for asymmetric cellar network. The incentive comprises double compensation, namely, global stimulating policy among coalitions (Inter-BEA) and local allocating rule within each coalition (Intra-BEA). We validate effectiveness of the proposed BEA scheme based on coalition game theory. Theoretical analysis and numerical simulation show that our techniques can provide enough motivation for nodes to relay for own profits, and thus enlarge system coverage.     

Distributed Coalition Formation Games for Secure Wireless Transmission

Cooperation among wireless nodes has been recently proposed for improving the physical layer (PHY) security of wireless transmission in the presence of multiple eavesdroppers. While existing PHY security literature answered the question “what are the link-level secrecy rate gains from cooperation?”, this paper attempts to answer the question of “how to achieve those gains in a practical decentralized wireless network and in the presence of a cost for information exchange?”. For this purpose, we model the PHY security cooperation problem as a coalitional game with non-transferable utility and propose a distributed algorithm for coalition formation. Using the proposed algorithm, the wireless users can cooperate and self-organize into disjoint independent coalitions, while maximizing their secrecy rate taking into account the costs during information exchange. We analyze the resulting coalitional structures for both decode-and-forward and amplify-and-forward cooperation and study how the users can adapt the network topology to environmental changes such as mobility. Through simulations, we assess the performance of the proposed algorithm and show that, by coalition formation using decode-and-forward, the average secrecy rate per user is increased of up to 25.3 and 24.4% (for a network with 45 users) relative to the non-cooperative and amplify-and-forward cases, respectively.     

Coalitional game‐based behavior analysis for spectrum access in cognitive radios

The core of cognitive radio paradigm is to introduce cognitive devices able to opportunistically access the licensed radio bands. The coexistence of licensed and unlicensed users prescribes an effective spectrum hole‐detection and a non‐interfering sharing of those frequencies. Collaborative resource allocation and spectrum information exchange are required but often costly in terms of energy and delay. In this paper, each secondary user (SU) can achieve spectrum sensing and data transmission through a coalitional game‐based mechanism. SUs are called upon to report their sensing results to the elected coalition head, which properly decides on the channel state and the transmitter in each time slot according to a proposed algorithm. The goal of this paper is to provide a more holistic view on the spectrum and enhance the cognitive system performance through SUs behavior analysis. We formulate the problem as a coalitional game in partition form with non‐transferable utility, and we investigate on the impact of both coalition formation and the combining reports costs. We discuss the Nash Equilibrium solution for our coalitional game and propose a distributed strategic learning algorithm to illustrate a concrete case of coalition formation and the SUs competitive and cooperative behaviors inter‐coalitions and intra‐coalitions. We show through simulations that cognitive network performances, the energy consumption and transmission delay, improve evidently with the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Novel distributed algorithm for coalition formation in cognitive radio networks for throughput enhancement using matching theory

In this paper, a novel algorithm is proposed for increasing the throughput in cognitive radio networks by forming coalitions among cognitive radio users in AWGN and fading channel environment. Although there have been numerous studies exploring the benefits of coalition formation in cognitive radio networks from the game theory perspective, there are several limitations in their application. To overcome the limitations of game theory, concepts from matching theory are used. Specifically, the stable marriage problem is used to formulate the interactions among the cognitive radio users as a matching game for collaborative distributed spectrum sensing under target detection probability constraint. The utility function is defined as the average probability of false alarm per cognitive radio user. The advantage of stable marriage is that it always converges to a stable matching and is Pareto optimal when the preferences of cognitive radios are strict. In the proposed model, the stable matching problem is extended to propose a novel algorithm to form coalitions of varying sizes for improving the utility of CR (false alarm and throughput). The coalitions formed using the algorithm are stable and do not deviate from the final matching. Using simulations and mathematical analysis, it is shown that the proposed algorithm leads to stable coalitions and returns significant improvement in terms of reduced probability of false alarm and improved throughput per cognitive radio user as compared to the noncooperative scenario.     

Distributed topology management for wireless multimedia sensor networks: exploiting connectivity and cooperation

In this paper, we propose a distributed topology management algorithm, named T‐Must, which orchestrates coalition formation game between camera and scalar sensor (SS) nodes, for use in wireless multimedia sensor networks. In the proposed solution, connectivity among the peer camera sensor (CS) nodes is maintained, and coverage is ensured between them. Only the scalar data are not sufficient to describe an event in a particular monitored area. In many cases, multimedia data (specifically, video data) are required to provide more precise information about the event. As the CS nodes, which sense and transmit multimedia data, are costlier than the SS nodes, the former are deployed in the monitored area in lesser numbers compared to the latter ones. In case of CS nodes, power consumption due to sensing is also significant, similar to power consumption for the transmission and reception of packets. Therefore, in this work, in order to increase the network lifetime, topology is controlled by forming coalition between the CS and SS nodes. Upon occurrence of an event, the SS nodes send scalar data to their associated CS nodes. If the scalar data received from SS nodes cross a preconfigured threshold, the associated CS node in the coalition starts sensing the event, captures the video data, and forwards the video data toward other coalitions or sink. Copyright © 2014 John Wiley & Sons, Ltd.     

On the tradeoff between altruism and selfishness in MANET trust management

Mobile ad hoc and sensor networks may consist of a mixture of nodes, some of which may be considered selfish due to a lack of cooperativeness in providing network services such as forwarding packets. In the literature, existing trust management protocols for mobile ad hoc networks advocate isolating selfish nodes as soon as they are detected. Further, altruistic behaviors are encouraged with incentive mechanisms. In this paper, we propose and analyze a trust management protocol for group communication systems where selfish nodes exist and system survivability is highly critical to mission execution. Rather than always encouraging altruistic behaviors, we consider the tradeoff between a node’s individual welfare (e.g., saving energy to prolong the node lifetime) vs. global welfare (e.g., achieving a given mission with sufficient service availability) and identify the best design condition of this behavior model to balance selfish vs. altruistic behaviors. With the system lifetime and the mission success probability as our trust-based reliability metric, we show that our behavior model that exploits the tradeoff between selfishness vs. altruism outperforms one that only encourages altruistic behaviors.     

Competitive Resource Sharing Based on Game Theory in Cooperative Relay Networks

This letter considers the problem of resource sharing among a relay and multiple user nodes in cooperative transmission networks. We formulate this problem as a sellers’ market competition and use a noncooperative game to jointly consider the benefits of the relay and the users. We also develop a distributed algorithm to search the Nash equilibrium, the solution of the game. The convergence of the proposed algorithm is analyzed. Simulation results demonstrate that the proposed game can stimulate cooperative diversity among the selfish user nodes and coordinate resource allocation among the user nodes effectively.     

基于虚拟联盟的重叠联盟形成算法

 重叠联盟形成问题是复杂智能系统中极具挑战性的前沿课题.特别是当一个能力有限的agent同时参与了多个不同的任务,但又不能同时满足这多个任务的需求时,就会产生资源冲突.为此,本文重点研究如何把一个无效的二维二进制编码修正为一个合法的编码,提出将有效联盟的剩余能力转移给一个动态的虚拟联盟,由虚拟联盟帮助解决其他无效联盟.实验结果表明,本文算法不会丢弃任何无效编码,在解决激烈的资源冲突时显得更加灵活有效.     

Algorithms for Transitive Dependence-Based Coalition Formation

Coalition formation methods allow autonomous agents to join together in order to act as a coherent group in which they increase their individual gains by collaborating with each other. Although there are some research efforts toward coalition formation in multiagent systems (MAS), such as game theory-based approaches, these methods cannot be easily applied in real-world scenarios. Based on a novel social reasoning theory, namely, transitive dependence theory, this work proposes two dynamic coalition formation algorithms for coalition formation: 1) without and-action dependence and 2) with and-action dependence, respectively. While most related work addresses the problem of searching for the optimal coalition structure (CS), the proposed algorithms aim to find out the optimal coalitions for specific goals. Theoretical analysis and experimental results suggest that 1) the algorithm for coalition formation without and-action dependence is of polynomial complexity and is efficient, and 2) when the incidence rate of and-action dependence is not high, the anytime algorithm for coalition formation with and-action dependence is also efficient although it has relatively high complexity (NP-complete).     

Building Multirobot Coalitions Through Automated Task Solution Synthesis

This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multirobot behaviors for accomplishing a multirobot task. We present the centralized anytime ASyMTRe configuration algorithm, proving that the algorithm is correct, and formally addressing issues of completeness and optimality. We then present a distributed version of ASyMTRe, called ASyMTRe-D, which uses communication to enable distributed coalition formation. We validate the centralized approach by applying the ASyMTRe methodology to two application scenarios: multirobot transportation and multirobot box pushing. We then validate the ASyMTRe-D implementation in the multirobot transportation task, illustrating its fault-tolerance capabilities. The advantages of this new approach are that it: 1) enables robots to synthesize new task solutions using fundamentally different combinations of sensors and effectors for different coalition compositions and 2) provides a general mechanism for sharing sensory information across networked robots.     

Task coalition formation and self‐adjustment in the wireless sensor networks

Coalition is an essential mechanism in the multi‐agent systems in the research of task‐oriented area. Self‐interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering‐based coalition formation and self‐adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX–MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self‐repairing autonomously. The stimulus‐response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd.     

Diffusion Recursive Least-Squares for Distributed Estimation Over Adaptive Networks

We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus, requiring a large amount of energy for communication. Incremental strategies that obtain the global solution have been proposed, but they require the definition of a cycle through the network. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution. We also show how to select the combination weights optimally.     

Fair Coalitions for Power-Aware Routing in Wireless Networks

Several power-aware routing schemes have been developed for wireless networks under the assumption that nodes are willing to sacrifice their power reserves in the interest of the network as a whole. But, in several applications of practical utility, nodes are organized in groups, and as a result, a node is willing to sacrifice in the interest of other nodes in its group but not necessarily for nodes outside its group. Such groups arise naturally as sets of nodes associated with a single owner or task. We consider the premise that groups will share resources with other groups only if each group experiences a reduction in power consumption. Then, the groups may form a coalition in which they route each other's packets. We demonstrate that sharing between groups has different properties from sharing between individuals and investigate fair, mutually beneficial sharing between groups. In particular, we propose a Pareto-efficient condition for group sharing based on max-min fairness called fair coalition routing. We propose distributed algorithms for computing the fair coalition routing. Using these algorithms, we demonstrate that fair coalition routing allows different groups to mutually beneficially share their resources     

Incremental augmented complex adaptive IIR algorithm for training widely linear ARMA model

In this paper, we propose a distributed adaptive learning algorithm to train the coefficients of a widely linear autoregressive moving average model by measurements collected by the nodes of a network. We assume that each node uses the augmented complex adaptive infinite impulse response (ACA-IIR) filter as the learning rule, and nodes interact with each other under an incremental mode of cooperation. To derive the proposed algorithm, called the incremental ACAIIR (IACA-IIR), we firstly formulate the distributed adaptive learning problem as an unconstrained minimization problem. Then, we apply stochastic gradient optimization argument to solve it and derive the proposed algorithm. We further find the step size range where the stability of the proposed algorithm is guaranteed. We also introduce a reduced-complexity version of the IACA-IIR algorithm. Since the proposed algorithm relies on the augmented complex statistics, it can be used to model both types of complex-valued signals (proper and improper signals). To evaluate the performance of the proposed algorithm, we use both synthetic and real-world complex signals in our simulations. The results exhibit superior performance of the proposed algorithm over the non-cooperative ACA-IIR algorithm.     

信任和效用关系约束的联盟结构生成

联盟结构生成是分布式人工智能的重要研究内容,一般仅依据智能体效用生成任意数量的联盟,这导致最优联盟结构生成的计算复杂度NP难。实际上,信任是合作的基础,信任关系对最终效用有直接的影响,应该综合考虑信任和效用关系。针对以上问题,该文扩展效用约束为信任和效用约束,用信任和效用二元组表示,以此作为联盟结构生成的依据。借鉴图割的s-t-cut算法,研究了基于信任和效用关系的联盟结构生成,在保证智能体个体理性和联盟稳定(无块)的前提下,使用信任和效用关系对网络进行切割,从而形成联盟。由此,该文提出了两种多项式时间的精确算法:信任关系约束下的MT-s-t-cut算法和信任效用关系约束下的MTU-s-t-cut算法,这两种算法均能够在多项式时间内得到最优联盟结构。仿真实验验证了信任关系影响所形成的联盟结构,社会整体效用随智能体数量的增加而增加,并且算法的运行时间远小于动态规划法(DP)和ODP-IP算法。     

Effect of Selfish Node Behavior on Efficient Topology Design

The problem of topology control is to assign per-node transmission power such that the resulting topology is energy-efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a non-cooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed and the network performance is comparable to that obtained from centralized algorithms.     

ReIDD: reliability-aware intelligent data dissemination protocol for broadcast storm problem in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have emerged as fast growing networks with aims to provide safety, and comfort to the onboard passengers. But, in this environment reliable data dissemination to the destination nodes is one of the biggest challenges as there may be a congestion in the network due to blind flooding of messages to their final destination, called as broadcast storm which may lead to the performance degradation with respect to the metric such as-message delivery, reliability and response time. To address this issue, in this paper, we propose a reliability-aware intelligent data dissemination protocol for broadcast storm problem in VANETs. We have solved the above specified problem using game theory concepts in which players, strategy space and decisions with respect to the current state of the system. To reduce the message overhead with respect to the communication cost among the players of the game, in the proposed scheme, messages are routed to the next destination by selecting the most reliable path in an intelligent manner. A coalition game is formulated among the vehicles by considering vehicles as the players in the game. Each player in the game has an initial payoff value based upon the parameters such as-communication range, storage requirements, and computation power. Based upon the payoff value of the players in the game, different coalitions/clusters are formulated among the players in the proposed scheme. Then, the players send the messages to the other vehicles/players within the same coalition which increases the reliability of transmission. In the proposed scheme, messages are unicasted to their final destination in an intelligent manner. Different algorithms are designed for coalition formation, maintenance, and reliability-aware data dissemination. The performance of the proposed scheme is evaluated using various evaluation metrics such as-service time, packet delivery ration, and throughput. The results obtained prove the effectiveness of the proposed scheme as compared to the other state-of-the-art existing schemes.     

Analytical Model of Cooperation in Ad Hoc Networks

The problem of cooperation among selfish nodes in ad hoc networks has gained recently a considerable attention. In this paper we propose a dynamic game theoretical model of cooperation in ad hoc networks, based on evolutionary game theory. Our model enables us to make predictions about possible equilibrium points of the network composed of the selfish and learning nodes, which can dynamically adjust their strategy in order to maximize their own payoff. In particular, we show that if an ad hoc network implements a reputation mechanism, all long term equilibrium points of the system will include cooperating nodes. In fact, in most of the equilibrium points, the cooperators will constitute a majority of the nodes. We believe that this new approach, borrowing from biological research, can have broader applications for studying dynamics of distributed communication systems.     

Joint resource allocation and interference management in the D2D uplink underlaying cellular networks

This paper investigates the problem of joint interference management and resource allocation in device-to-device (D2D) uplink underlaying cellular networks. To improve the D2D system sum throughput, an overlapping coalition formation game (OCFG) is proposed to achieve resource allocation. In the game, a novel initialization in term of the priority sequence is proposed to rapidly form the initial coalitions. During the coalition formation, the merging and splitting sequences are adopted to guide the order that D2D users merge in or split from the coalition. Here the splitting sequences are formed according to cross-tier interference strength and the merging sequence is based on matching theory. Besides, a power control scheme is formulated to optimize the power allocation. The performance of the proposed algorithm is analyzed and verified through simulations.