Forwarding Game Research Articles

Packet Forwarding in Common Sink Multi-Domain Wireless Sensor Networks Using Non-Cooperative Game

Multi-domain wireless sensor networks (WSNs) are sensor networks belonging to different network authorities coexisting in the same area. As such networks may be deployed in hostile environments, resource sharing between different sensor networks may necessary to prolong the network lifetime. However, with their limited power supply, sensor nodes may act selfishly by declining to cooperate to conserve their own energy. This paper is focused on how to determine packet forwarding strategies decisions which are beneficial to all networks under constrained resources in multi-domain WSNs. This paper proposes a non-cooperative game algorithm in a packet forwarding game in non-cooperative multi-domain WSNs. The objective is to achieve the best mutual strategy between two multi-domain WSNs. Our results show that the proposed method outperforms the non-game theoretic approach which uses pure cooperative and non-cooperative AODV routing algorithms, in terms of successful packet delivery ratio, network lifetime, and fairness in terms of difference in energy consumption in each domain.

Packet Forwarding Strategies in Multiagent Systems: An Evolutionary Game Approach

In multiagent systems (MASs), agents need to forward packets to each other to accomplish a target task. In this paper, we study packet forwarding among agents using evolutionary game theory under the mechanisms of Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Packet forwarding among agents plays a key role to stabilize the whole MAS. We study the transfer probability of packet forwarding of agents at the idle state or the busy state and computer the probability of the packet forwarding for a MAS. When agents make their decisions to select Forward or No-Forward strategy, a packet forwarding evolutionary game model is built to reflect the utilities of different packet forwarding strategies. Two incentive mechanisms are introduced into the game model. One is to motivate agents to strengthen cooperation; the other is to encourage agents to select the No-Forward strategy to save energy while they are in the busy state. The parameter value that encourages an agent to select the No‐Forward strategy is inversely proportional to the average probability of the packet forwarding. The replicator dynamics of agent packet forwarding strategy evolution are given. We propose and prove the theorems indicating that evolutionarily stable strategies (ESSs) can be attained. The results of simulation experiments verify the correctness of the proposed theorems and the effects of the two incentive mechanisms and the probability of packet forwarding, which assures the robustness of evolutionary stable points among agents in MASs.

Fair Route Selection in Multi-Domain WSNs using Non-Cooperative Game Theory in Separate Sink Scenario

An important use of multi-domain wireless sensor networks (WSNs) is resource sharing between different networks co-existing in the same area to prolong the network lifetime. The challenge of resource allocation in such scenario is how to determine packet forwarding strategies which are beneficial to all networks under constrained resources in non-cooperative multi-domain WSNs. Therefore, this paper proposes the Non-cooperative game algorithm based on Lemke Howson method (NCG-LH) for a packet forwarding game in non-cooperative multi-domain WSNs. The objective is to achieve the best mutual strategy and improve the network performance between two different network authorities in non-cooperative multi-domain WSNs in separate sink scenario. Results show that NCG-LH can obtain longer network lifetime than the existing routing algorithms particularly in presence of failed nodes and high path loss. NCG-LH also outperforms the other routing algorithms in terms of fair route selection by attaining the lowest average difference in energy consumption.

Study on Selfish Node Incentive Mechanism with a Forward Game Node in Wireless Sensor Networks

In a wireless sensor network, some nodes may act selfishly and noncooperatively, such as not forwarding packets, in response to their own limited resources. If most of the nodes in a network exhibit this selfish behavior, the entire network will be paralyzed, and it will not be able to provide normal service. This paper considers implementing the idea of evolutionary game theory into the nodes of wireless sensor networks to effectively improve the reliability and stability of the networks. We present a new model for the selfish node incentive mechanism with a forward game node for wireless sensor networks, and we discuss applications of the replicator dynamics mechanism to analyze evolutionary trends of trust relationships among nodes. We analyzed our approach theoretically and conducted simulations based on the idea of evolutionary game theory. The results of the simulation indicated that a wireless sensor network that uses the incentive mechanism can forward packets well while resisting any slight variations. Thus, the stability and reliability of wireless sensor networks are improved. We conducted numerical experiments, and the results verified our conclusions based on the theoretical analysis.

Cognitive forwarding control in wireless ad-hoc networks with slow fading channels

We propose a decentralized stochastic control solution for the broadcast message dissemination problem in wireless ad-hoc networks with slow fading channels. We formulate the control problem as a dynamic robust game which is well-justified by two key observations: first, the shared nature of the wireless medium which inevitably cross-couples the nodes’ forwarding decisions, thus binding them together as strategic players; second, the stochastic dynamics associated with the link qualities which renders the transmission costs noisy, thus motivating a robust formulation. Given the non-stationarity induced by the fading process, an online solution for the formulated game would then require an adaptive procedure capable of both convergence to and tracking strategic equilibria as the environment changes. To this end, we deploy the strategic and non-stationary learning algorithm of regret-tracking, the temporally-adaptive variant of the celebrated regret-matching algorithm, to guarantee the emergence and active tracking of the correlated equilibria in the dynamic robust forwarding game. We also make provision for exploiting the channel state information, when available, to enhance the convergence speed of the learning algorithm by conducting an accurate (transmission) cost estimation. This cost estimate can basically serve as a model which spares the algorithm from extra action exploration, thus rendering the learning process more sample-efficient. Simulation results reveal that our proposed solution excels in terms of both the number of transmissions and load distribution while also maintaining near perfect delivery ratio, especially in dense crowded environments.

Equilibrium Flows and Path Dilation for a Network Forwarding Game

We consider a forwarding game on directed graphs where selfish nodes need to send certain amount of flow (packets) to specific destinations, possibly through several relay nodes. Each node has to decide whether to pay the cost of relaying flow as an intermediate node, given the fact that its neighbors can punish it for its non-cooperation. In this work we simplify the original network model, and provide the first experimental evaluation of these equilibria for different classes of graphs. We provide clear evidence that these equilibrium solutions are indeed significant and establish how these equilibria depend on various properties of the network such as average degrees and flow demand density. Our main results establish experimental bounds for the path dilation in this model: the average ratio of the routed flow cost at equilibrium over the cost of the optimal routing which would involve shortest path routing in our model.

An MPS-BNS Mixed Strategy Based on Game Theory for Wireless Mesh Networks

To achieve a valid effect of wireless mesh networks against selfish nodes and selfish behaviors in the packets forwarding, an approach named mixed MPS-BNS strategy is proposed in this paper. The proposed strategy is based on the Maximum Payoff Strategy (MPS) and the Best Neighbor Strategy (BNS). In this strategy, every node plays a packet forwarding game with its neighbors and records the total payoff of the game. After one round of play, each player chooses the MPS or BNS strategy for certain probabilities and updates the strategy accordingly. In MPS strategy, each node chooses a strategy that will get the maximum payoff according to its neighbor's strategy. In BNS strategy, each node follows the strategy of its neighbor with the maximum total payoff and then enters the next round of play. The simulation analysis has shown that MPS-BNS strategy is able to evolve to the maximum expected level of average payoff with faster speed than the pure BNS strategy, especially in the packets forwarding beginning with a low cooperation level. It is concluded that MPS-BNS strategy is effective in fighting against selfishness in different levels and can achieve a preferable performance.

Optimal Forwarding Games in Mobile Ad Hoc Networks with Two-Hop f-cast Relay

This paper examines the optimal forwarding problem in mobile ad hoc networks (MANETs) based on a generalized two-hop relay with limited packet redundancy f (f-cast) for packet routing. We formulate such problem as a forwarding game, where each node i individually decides a probability τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (i.e., a strategy) to deliver out its own traffic and helps to forward other traffic with probability 1-τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ∈[0,1], while its payoff is the achievable throughput capacity of its own traffic. We derive closed-form result for the per node throughput capacity (i.e., payoff function) when all nodes play the symmetric strategy profiles, identify all the possible Nash equilibria of the forwarding game, and prove that there exists a Nash equilibrium strategy profile that is strictly Pareto optimal. Finally, for any symmetric profile, we explore the possible maximum per node throughput capacity and determine the corresponding optimal setting of f to achieve it.

Evolutionary forwarding games in delay tolerant networks: Equilibria, mechanism design and stochastic approximation

In this paper, we apply evolutionary games to non-cooperative forwarding control in Delay Tolerant Networks (DTNs). The main focus is on mechanisms to rule the participation of the relays to the delivery of messages in DTNs. Thus, we express the success probability as a function of the competition that takes place within a large population of mobiles, and we characterize the effect of reward-based mechanisms on the performance of such systems. Devices acting as active relays, in fact, sacrifice part of their batteries in order to support message replication and thus increase the probability to reach the destination. In our scheme, a relay can choose the strategy by which they participate to the message relaying. A mobile that participates receives a unit of reward based on the reward mechanism selected by the network. A utility function is introduced as the difference between the expected reward and the energy cost, i.e., the cost spent by the relay to sustain forwarding operations. We show how the evolution dynamics and the equilibrium behavior (called Evolutionary Stable Strategy – ESS) are influenced by the characteristics of inter contact time, energy expenditure and pricing characteristics.We extend our analysis to mechanisms that the system can introduce in order to have the message delivered to the destination with high probability within a given deadline and under energy constraints which bound the number of released copies per message. Finally, we apply our findings in order to devise decentralized forwarding algorithms that are rooted in the theory of stochastic approximations. Thus, we demonstrate that the ESS can be attained without complete knowledge of the system state and letting the source monitor number of released copies per message only. We provide extensive numerical results to validate the proposed scheme.

A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI) attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females) with significant inattentive symptoms (combined and inattentive ADHD subtypes). This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was −4.6 (5.9) and −4.7 (5.6) respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01). Cohen’s d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters). Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD.Trial RegistrationClinicalTrials.gov NCT01344044

Indirect Reciprocity Game Modelling for Cooperation Stimulation in Cognitive Networks

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is I help you not because you have helped me but because you have helped others. We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

Cooperative strategies of wireless access technologies: A game-theoretic analysis

The wireless Internet has to overcome the problem of spectrum scarcity as the number of mobile equipments could increase even by an order of magnitude in the next decade; the cooperation of mobile devices is foreseeable as a feasible solution to the problems. There exists a large body of literature on opportunistic ad hoc networking including Pelusi et al. (2006) [25], Chen et al. (2006) [26], Hui et al. (2005) [27]; however, the impact of the location of the devices on their access method selection is not yet appropriately dealt with. In this paper, we address this issue based on game-theoretic analyses. The key contribution of our work is threefold. First, we model the access method selection of mobile devices by extending the classical forwarding game with position, mobility, and availability of the devices. Second, we apply the model in game-theoretic analyses to better understand the optimal cooperation strategies in the presence of heterogeneous wireless technologies. We further extend our framework to include uncertainty. Finally, we present the applicability of the model in a cognitive radio scenario where complex structures of parameters are included.

Secure Cooperation in Autonomous Mobile Ad-Hoc Networks Under Noise and Imperfect Monitoring: A Game-Theoretic Approach

In autonomous mobile ad-hoc networks, one major challenge is to stimulate cooperation among selfish nodes, especially when some nodes may be malicious. In this paper, we address cooperation stimulation in realistic yet challenging contexts where the environment is noisy and the underlying monitoring is imperfect. We have first explored the underlying reasons why stimulating cooperation under such scenarios is difficult. Instead of trying to enforce all nodes to act fully cooperatively, our goal is to stimulate cooperation in a hostile environment as much as possible through playing conditional altruism. To formally address the problem, we have modeled the interactions among nodes as secure routing and packet forwarding games under noise and imperfect observation, and devised a set of reputation-based attack-resistant cooperation strategies without requiring any tamper-proof hardware or central banking service. The performance of the devised strategies has also been evaluated analytically. The limitations of the game-theoretic approaches and the practicability of the devised strategies have also been investigated through theoretical analysis and extensive simulation studies. The results have demonstrated that although sometimes a gap may exist between the ideal game model and the reality, game-theoretic analysis can still provide thoughtful insights and useful guidelines when designing cooperation strategies.

Best neighbor strategy to enforce cooperation among selfish nodes in wireless ad hoc network

Trust among nodes in a self-organizing network such as a mobile ad hoc network presents a number of problems and paradoxes. One of the challenging characteristics of wireless and mobile ad hoc networks consists in exploring ways to cope up with selfish behavior of neighbors towards network functions such as routing and forwarding. This paper attempts to deal with such mechanisms and as a result it introduces a distinct model to study the behavior of selfish neighbors using strategic, non-cooperative game theory. Many research works have used Tit-For-Tat strategy for analysis when they deploy game theory to stimulate cooperation. A compliant and an adaptable strategy called Best Neighbor Strategy [BNS] is proposed in this paper for the packet forwarding game in a wireless ad hoc environment. The behavior of nodes is probed varying the proportion of selfishness and also the size of the population while forwarding the packets. The investigations have brought out that the proposed cooperation enforcement policy is scalable, is able to converge faster and is robust against selfishness. BNS achieves evolutionary stability even under the invasion of selfish strategy at different proportions. Further, BNS proves to be a pure evolutionary stable strategy as it evolves to dominate the population from whatever the initial frequency it starts with and it totally out-competes the malign behavior shown by selfish strategy, which means that BNS is immune to invaders. The observations and analysis have shown that the ad hoc paradigm can be modeled significantly using an approach, which has been developed for game theory.

Securing Cooperative Ad-Hoc Networks Under Noise and Imperfect Monitoring: Strategies and Game Theoretic Analysis

In cooperative ad-hoc networks, nodes belong to the same authority and pursue the common goals, and will usually unconditionally help each other. Consequently, without necessary countermeasures, such networks are extremely vulnerable to insider attacks, especially under noise and imperfect monitoring. In this paper, we present a game theoretic analysis of securing cooperative ad-hoc networks against insider attacks in the presence of noise and imperfect monitoring. By focusing on the most basic networking function, namely routing and packet forwarding, we model the interactions between good nodes and insider attackers as secure routing and packet forwarding games. The worst case scenarios are studied where initially good nodes do not know who the attackers are while insider attackers know who are good. The optimal defense strategies have been devised in the sense that no other strategies can further increase the good nodes' payoff under attacks. Meanwhile, the optimal attacking strategies and the maximum possible damage that can be caused by attackers have been discussed. Extensive simulation studies have also been conducted to evaluate the effectiveness of the proposed strategies

Game Theoretic Analysis of Cooperation Stimulation and Security in Autonomous Mobile Ad Hoc Networks

In autonomous mobile ad hoc networks, nodes belong to different authorities and pursue different goals; therefore, cooperation among them cannot be taken for granted. Meanwhile, some nodes may be malicious, whose objective is to damage the network. In this paper, we present a joint analysis of cooperation stimulation and security in autonomous mobile ad hoc networks under a game theoretic framework. We first investigate a simple yet illuminating two-player packet forwarding game and derive the optimal and cheat-proof packet forwarding strategies. We then investigate the secure routing and packet forwarding game for autonomous ad hoc networks in noisy and hostile environments and derive a set of reputation-based cheat-proof and attack-resistant cooperation stimulation strategies. When analyzing the cooperation strategies, besides Nash equilibrium, other optimality criteria, such as Pareto optimality, subgame perfection, fairness, and cheat-proofing, have also been considered. Both analysis and simulation studies have shown that the proposed strategies can effectively stimulate cooperation among selfish nodes in autonomous mobile ad hoc networks under noise and attacks, and the damage that can be caused by attackers is bounded and limited

On designing incentive-compatible routing and forwarding protocols in wireless ad-hoc networks

In many applications, wireless ad-hoc networks are formed by devices belonging to independent users. Therefore, a challenging problem is how to provide incentives to stimulate cooperation. In this paper, we study ad-hoc games--the routing and packet forwarding games in wireless ad-hoc networks. Unlike previous work which focuses either on routing or on forwarding, this paper investigates both routing and forwarding. We first uncover an impossibility result--there does not exist a protocol such that following the protocol to always forward others' traffic is a dominant action. Then we define a novel solution concept called cooperation-optimal protocols. We present Corsac, a cooperation-optimal protocol which consists of a routing protocol and a forwarding protocol. The routing protocol of Corsac integrates VCG with a novel cryptographic technique to address the challenge in wireless ad-hoc networks that a link's cost (i.e., its type) is determined by two nodes together. Corsac also applies efficient cryptographic techniques to design a forwarding protocol to enforce the routing decision, such that fulfilling the routing decision is the optimal action of each node in the sense that it brings the maximum utility to the node. We evaluate our protocols using simulations. Our evaluations demonstrate that our protocols provide incentives for nodes to forward packets. Additionally, we discuss the challenging issues in designing incentive-compatible protocols in ad hoc networks.

Best Foot Forward or Waiting Game: First Mover Effects in a Distributive Negotiation

This study reports the results of an experiment designed to test the premise of a first mover advantage in distributive bargaining. Dyads negotiated the price of an advance for a newly published book in the absence of market information. The experiment explored the effects of advice to make an initial offer before your opponent or to wait until your opponent reveals their initial offer. A first mover advantage was evident across all conditions. Sellers in dyads in which one of the negotiators was instructed to move first benefited from making the first offer. If the first mover landed their initial offer in the bargaining zone, the first mover advantage became a liability. Implications for theory and negotiation practice are discussed.

HYSTERESIS, MARKET FORCES, AND THE ROLE OF POLICY IN A DYNAMIC GAME WITH INSIDERS AND OUTSIDERS

THIS paper investigates some consequences of treating seriously the distinction between insiders and outsiders. By insiders I mean incumbent workers with previous work experience with a particular firm. Outsiders are those without employment or newcomers who have just been hired by a firm and are beginning the transition to being future insiders. To make this distinction as sharp as possible, I assume that insiders derive their market power from firm-specific training and experience so that the firm cannot augment its stock of insiders by buying in experienced workers from other firms. Clearly this assumption is extreme-for example one need think only of the golden hellos which were offered to financial analysts hopping between firms in London during Big Bang-but it has the merit of establishing an upper bound on the market power incumbent workers could conceivably wield. In the world I wish to contemplate present hiring determines the firm's future stock of insiders and time is of the essence. In such a framework, it is natural to model bargaining as a forward looking dynamic game. Indeed it would be quite inappropriate to model the bargain in static terms and then append some dynamics as an afterthought. Dynamic games quickly become complicated and intractable yet it is desirable to formulate a model in which clear analytical solutions can be obtained. In this paper I adopt the device of a simple overlapping generations model in which workers can work at most two periods. Newcomers N begin work in the first period of their lives. If they remain with their firm in the next period they become insiders I. In each period the firm earns revenue R from its insiders and newcomers. To establish an upper bound on insider power, I adopt a particular formulation of the familiar monopoly union model. Specifically, I assume that insiders earn a wage w and newcomers a wage x. Current insiders are the union in the sense that they make the firm a unilateral wage offer (w, x) subject to which the firm then makes employment decisions. Thus the purpose of the paper is not to explain the particular form of bargaining equilibrium between the firm and its workforce but to investigate the nature of equilibrium when incumbent workers set wages in a dynamic game representation of the monopoly union model. Here, the two period