In this paper, we consider an orthogonal Multiple Access Multiple Relay Network (MAMRN), where several nodes cooperate to send their messages to a single destination. Frequency Division Multiplexing (FDM) is adopted for the sources and the relays, and the destination acts as the central node responsible for scheduling the cooperative retransmissions. Since FDM is used for orthogonality, different nodes are allocated at each sub-band of the transmission and retransmission phases. In this work, we present the system model of the considered orthogonal MAMRN when the FDM mechanism is adopted, while including the analytical derivations of the utility metrics (spectral efficiency and outage events). Then, two centralized node selection strategies are proposed. Moreover, we present the control information exchange process between the destination and the different nodes. The proposed strategies allocate for each sub-band the node that will transmit (or retransmit) with the goal of maximizing the spectral efficiency. Our numerical analysis considers both symmetric and asymmetric channel and source rate scenarios, and shows that the proposed algorithms outperform the algorithms used in the prior-art, and achieve a spectral efficiency that is close to the upper bound calculated by an exhaustive search approach while reducing the complexity and the overhead.

In this paper, we propose a novel selection strategy for the orthogonal Multiple Access Multiple Relay Networks (MAMRN). Rather than selecting a single relaying node to help one source node at a given retransmission time slot, we propose allocating one source to be helped by multiple relaying nodes. The idea is to exploit the multipath diversity of the different relaying nodes in order to optimize the spectral efficiency. We present the control exchange process in the novel selection strategy and we compare it to that of the prior art. In addition, we investigate the effect of equal gain combining on the performance, as well as the effect of the rates and the channel configuration. The numerical results show that the proposed strategy outperforms the prior art by exploiting the power budget available at each relaying node included in the system.

Practically, a botnet is spread over the Internet of Things (IoT) to ensure an attacker the control of a large number of devices. In this context, in which IoT users react to protect their devices against the threat, a zero-sum one-sided partially observable stochastic game (OS-POSG) model is proposed in which a defender strategically places honeypots in the IoT network in order to deceive attacker’s actions and mitigate the botnet propagation. No player (attacker and defender) observes the opponent’s action but, realistically, the attacker—who is the maximizer—has a perfect knowledge of the state of the network while the defender—who is the minimizer—only is informed of the decisions of IoT users. The objective is to find an optimal deception strategy for the defender that better limits from above the proportion of infected IoT devices. We show in numerous simulations the impact of the partial observation and of the strategic defender’s action on the particular metrics which are the maximum proportion of infected IoT devices during the botnet propagation and the time to botnet extinction in the IoT network.

It has been a long demand of Internet Service Providers (ISPs) that the Content Providers (CPs) share their profits for investments in network infrastructure. In this paper, we study profit sharing contracts between a CP with multiple ISPs. Each ISP commits to improving the Quality of Service (QoS) for the end-users through higher investments efforts. The CP agrees to share the profits due to the resulting higher demand for its content. We first model non-cooperative interaction between the CP and the ISPs as a two-stage Stackelberg game. CP is the leader that decides what fraction of its profits will be shared with the ISPs. Each ISP then simultaneously decides the amount of effort (investment) to enhance network quality. Here, CP cannot observe individual effort by the ISPs, which poses a challenge for the CP to decide how to share the profits with each ISP. Therefore, we also investigate a cooperative scenario, where the CP only decides the total share it gives to the ISPs, and each ISP then cooperatively shares the profit among themselves. We study the effect of such cooperation between the ISPs by building a Nash Bargaining based model. We show that the collaboration improves total effort by the ISPs and the payoff of the CP.

In this paper, we describe an iterative scheme which is able to estimate the Fiedler value of a network when the topology is initially unknown. The only available information is the one obtained through a random walk process over the network. Our algorithm is based on the Rayleigh quotient optimization problem and the theory of stochastic approximation. We explain the different tools used to construct our algorithm and we describe our iterative scheme. Finally, we illustrate its performance through a numerical study.

Electric Vehicles (EV) impact urban networks both when driving (e.g., noise and pollution reduction) and charging. For the electrical grid, the flexibility of EV charging makes it a significant actor in “Demand Response” mechanisms. Therefore, there is a need to design incentive mechanisms to foster customer engagement. A congestion game approach is adopted to evaluate the performance of such electrical transportation system with multiple classes of vehicles: EV and Gasoline Vehicles. Both temporal and energy operating costs are considered. The latter is nonseparable as it depends on the global charging need of all EV, which is scheduled in time by a centralized aggregator in function of nonflexible consumption at charging location. Thus, driving and charging decisions are coupled. An adaptation of Beckmann’s method proves the existence of a Wardrop Equilibrium (WE) in the considered nonseparable congestion game; this WE is unique when the charging unit price is an increasing function of the global charging need. A condition on the nonflexible load is given to guarantee the monotonicity of this function. This condition is tested on real consumption data in France and in Texas, USA. Optimal tolls are used to control this electrical transportation system and then computed in order to minimize an environmental cost on a simple network topology.

The ongoing net neutrality debate has generated a lot of heated discussions on whether or not monetary interactions should be regulated between content and access providers. Among the several topics discussed, `differential pricing' has recently received attention due to `zero-rating' platforms proposed by some service providers. In the differential pricing scheme, Internet Service Providers (ISPs) can exempt data access charges for on content from certain CPs (zero-rated) while no exemption is on content from other CPs. This allows the possibility for Content Providers (CPs) to make `sponsorship' agreements to zero-rate their content and attract more user traffic. In this article, we study the effect of differential pricing on various players in the Internet. We first consider a model with a monopolistic ISP and multiple CPs where users select CPs based on the quality of service (QoS) and data access charges. We show that in a differential pricing regime 1) it is possible for a CP to obtain higher utility than a CP offering better QoS through higher subsidy at user equilibrium 2) Overall QoS (mean delay) for end users can degrade under differential pricing schemes. In the oligopolistic market with multiple ISPs, ISPs tend to set equal access prices at equilibrium and similar conclusions are derived as in the monopolistic market.

In this paper, we consider a “generalized” fractional program in order to solve a popularity optimization problem in which a source of contents controls the topics of her contents and the rate with which posts are sent to a time line. The objective of the source is to maximize its overall popularity in an Online Social Network (OSN). We propose an efficient algorithm that converges to the optimal solution of the Popularity maximization problem.

This paper proposes and analyzes a stochastic multiagent opinion dynamics model. We are interested in a multi-leveled opinion of each agent which is randomly influenced by the binary actions of its neighbors. It is shown that, as far as the number of agents in the network is finite, the model asymptotically produces consensus. The consensus value corresponds to one of the absorbing states of the associated Markov system. However, when the number of agents is large, we emphasize that partial agreements are reached and these transient states are metastable, i.e., the expected persistence duration is arbitrarily large. These states are characterized using an N-intertwined mean field approximation (NIMFA) for the Markov system. Numerical simulations validate the proposed analysis.

The ongoing net neutrality debate has generated a lot of heated discussions on whether or not monetary interactions should be regulated between content and access providers. Among the several topics discussed, `differential pricing' has recently received attention due to `zero-rating' platforms proposed by some service providers. In the differential pricing scheme, Internet Service Providers (ISPs) can exempt data traffic charges for accessing content from certain Content Providers (CPs) or applications (zero-rated) and apply regular charges for accessing content from other CPs. This allows the possibility for CPs to make `sponsorship' agreements to zero-rate their content and attract more user traffic. In this paper, we study the effect of differential pricing on various players in the Internet. We consider a model with a single ISP and multiple CPs where users select CPs based on the quality of service (QoS) and applicable traffic charges. We show that in a differential pricing regime 1) a CP offering low QoS can make more revenues than a CP offering better QoS through sponsorships. 2) QoS (mean delay) for end users can degrade compared to the case where no differential pricing is allowed.

Throughput of a mobile ad hoc network (MANET) operating on an unlicensed spectrum can increase if nodes can also transmit on a (shared) licensed spectrum. However, the transmissions on the licensed spectrum has to be limited to avoid degradation of quality of service (QoS) to primary users (PUs). We address the problem of how the nodes of a MANET or secondary users (SUs) should spread their transmissions on both licensed and unlicensed spectra to maximize network throughput, and characterize ‘throughput gain’ achieved in such spectrum sharing systems. We show that the gain can be significant and is increasing in the density of the SUs. The primary and secondary users are modeled as two independent Poisson point processes and their performance is evaluated using techniques from stochastic geometry.

Cyber insurance is a valuable approach to mitigate further the cyber risk and its loss in addition to the deployment of technological cyber defense solutions, such as intrusion detection systems and firewalls. An effective cyber insurance policy can reduce the number of successful cyber attacks by incentivizing the adoption of preventative measures and the implementation of best practices of the users. To study cyber insurance in a holistic manner, we first establish a bi-level game-theoretic model that nests a zero-sum game in a moral-hazard type of principal-agent game to capture complex interactions between a user, an attacker, and the insurer. The game framework provides an integrative view of the cyber insurance and enables a systematic design of incentive compatible and attack-aware insurance policy. The framework is further extended to study a network of users and their risk interdependencies. We completely characterize the equilibrium solutions of the bi-level game. Our analytical results provide a fundamental limit on insurability, predict the Peltzman effect, and reveal the principles of zero operating profit and the linear insurance policy of the insurer. We provide analytical results and numerical experiments to corroborate the analytical results and demonstrate the network effects as a result of the strategic interactions among the three types of players.

Malware is increasingly sophisticated and affects the wellbeing of a large population of heterogeneous and highly connected devices. The users of these devices can make strategic and dynamic decisions to choose whether or not to adopt the antivirus software, not only to secure their individual devices but also to protect the network they are part of. Motivated by the strategic behaviors of the antivirus adoption, we establish an evolutionary Poisson game framework to capture the random, dynamic, and heterogeneous interactions of agents in a holistic fashion, and design mechanisms to control their behaviors to achieve a system-wide objective. We first prove the existence and uniqueness of a mixed Nash equilibrium of the large population game and show that the equilibrium is an evolutionary stable strategy. Finally, we develop online algorithms using the techniques of stochastic approximation coupled with the population dynamics, and they are shown to converge to the optimal solution of the controller problem. Numerical examples are used to illustrate and corroborate our results.

The emphasis of this paper is put on 5G multi-cell networks which are composed of dense and mutually interfering evolved NodeBs (eNBs) sharing the scarce radio resources. Consequently, greater focus is given to resource management techniques that take Inter-Cell Interference (ICI) into account, in particular to power control. Beside power control, this paper tackles also user association and scheduling. Despite the relevance of the addressed problem, it has remained largely unsolved, mainly due to its non-convex and combinatorial nature. We address this multifaceted challenge in a distributed fashion for reduced complexity.

In this paper, we propose a hierarchical game approach to model the energy efficiency maximization problem, where transmitters individually choose their channel assignment and power control. We conduct a thorough analysis of the existence, uniqueness, and characterization of the Stackelberg equilibrium. Interestingly, we formally show that a spectrum orthogonalization naturally occurs when users decide sequentially about their transmitting carriers and powers, delivering a binary channel assignment. Both analytical and simulation results are provided for assessing and improving the performances in terms of energy efficiency and spectrum utilization between the simultaneous-move game (with synchronous decision makers), the social welfare (in a centralized manner), and the proposed Stackelberg (hierarchical) game. For the first time, we provide tight closed-form bounds on the spectral efficiency of such a model, including correlation across carriers and users. We show that the spectrum orthogonalization capability induced by the proposed hierarchical game model enables the wireless network to achieve the spectral efficiency improvement while still enjoying a high energy efficiency.

In this paper, we have two objectives: First we model the posting behavior in Social Networks in topics which have negative externalities, and the second objective is to propose content active filtering in order to increase content diversity. By negative externalities, we mean that when the quantity of posted contents about some topic increases the popularity of posted contents decreases. We introduce a dynamical model to describe the posting behavior of users taking into account these externalities. Our model is based on stochastic approximations and sufficient conditions are provided to ensure its convergence to a unique rest point. We provide a close form of this rest point. Content Active Filtering (CAF) are actions taken by the administrator of the Social Network in order to promote some objectives related to the quantity of contents posted in various topics. As objective of the CAF we shall consider maximizing the diversity of posted contents.

Enabling coordination between products from different vendors is a key characteristic of the design philosophy behind future wireless communication networks. As an example, different devices may have different implementations, leading to different user experiences. A similar story emerges when devices running different physical and link layer protocols share frequencies in the same spectrum in order to maximize the system-wide spectral efficiency. In such situations, coordinating multiple interfering devices presents a significant challenge not only from an interworking perspective (as a result of reduced infrastructure), but also from an implementation point of view. The following question may then naturally arise: How to accommodate integrating such heterogeneous wireless devices seamlessly? One approach is to coordinate the spectrum in a centralized manner. However, the desired autonomous feature of future wireless systems makes the use of a central authority for spectrum management less appealing. Alternately, intelligent spectrum coordination have spurred great interest and excitement in the recent years. This paper presents a multi-level (hierarchical) power control game where users jointly choose their channel and power control selfishly in order to maximize their individual energy efficiency. By hierarchical, we mean that some users' decision priority is higher/lower than the others. We propose two simple and nearly-optimal algorithms that ensure complete spectrum coordination among users. Interestingly, it turns out that the complexity of the two proposed algorithms is, in the worst case, quadratic in the number of users, whereas the complexity of the optimal solution (obtained through exhaustive search) is N!. These results offer hope that such simple and accurate power control algorithms can be designed around competition, as hierarchical behavior does not only improve the mechanism's performance but also leads to simpler distributed power control algorithms.

High data rates are essential for next-generation wireless networks to support a growing number of computing devices and networking services. Small cell base station (SCBS) (e.g., picocells, microcells, femtocells) technology is a cost-effective solution to address this issue. However, one challenging issue with the increasingly dense network is the need for a distributed and scalable access point association protocol. In addition, the reduced cell size makes it easy for an adversary to map out the geographical locations of the mobile users, and hence breaching their location privacy. To address these issues, we establish a game-theoretic framework to develop a privacy-preserving stable matching algorithm that captures the large scale and heterogeneity nature of 5G networks. We show that without the privacy-preserving mechanism, an attacker can infer the location of the users by observing wireless connections and the knowledge of physical-layer system parameters. The protocol presented in this work provides a decentralized differentially private association algorithm which guarantees privacy to a large number of users in the network. We evaluate our algorithm using case studies, and demonstrate the tradeoff between privacy and system-wide performance for different privacy requirements and a varying number of mobile users in the network. Our simulation results corroborate the result that the total number of mobile users should be lower than the overall network capacity to achieve desirable levels of privacy and QoS.

Smooth and green future extension/scalability (e.g., from sparse to dense, from small-area dense to large-area dense, or from normal-dense to super-dense) is an important issue in heterogeneous networks. In this paper, we study energy efficiency of heterogeneous networks for both sparse and dense (two-tier and multi-tier) small cell deployments. We formulate the problem as a hierarchical (Stackelberg) game in which the macro cell is the leader whereas the small cell is the follower. Both players want to strategically decide on their power allocation policies in order to maximize the energy efficiency of their registered users. A backward induction method has been used to obtain a closed-form expression of the Stackelberg equilibrium. It is shown that the energy efficiency is maximized when only one sub-band is exploited for the players of the game depending on their channel fading gains. Simulation results are presented to show the effectiveness of the proposed scheme.

The intense throughput and stringent delay requirements of Internet multimedia applications has spurred the need for new transport protocols with flexible transmission control. Current TCP congestion control adopts an Additive Increase Multiplicative Decrease (AIMD) algorithm that linearly increases or exponentially decreases the congestion window based on transmission acknowledgements. In this paper, we propose an AIMD-based media-aware congestion control that determines the optimal congestion window updating policy for multimedia transmission. The media-aware congestion control is formulated as a Partially Observable Markov Decision Process (POMDP), which maximizes the long-term expected quality of the received multimedia data. Moreover, we propose a reinforcement learning algorithm in order to estimate the environment and adapt to the source and network variations on the fly. Simulation results show that the proposed approach can significantly improve the received video quality, particularly at high source rates, compared to conventional TCP.

In this paper, we propose an algorithmic perspective of the Stackelberg game model introduced in [1] applied to cognitive radio networks (CRN). Typically, we assume that individual users attempt to access to the wireless spectrum while maximizing their individual energy efficiency. Having looked at the main properties of the proposed energy efficient and in particular the one related to spectrum coordination, we address the problem of sensing. Then, we provide a deep algorithmic analysis on how primary and secondary users can reach such a spectrum coordination using an appropriate learning process. We validate our results through extensive simulations and compare the proposed algorithm to some typical scenarios including the non-cooperative case in [2] and the throughput-based-utility systems. Specifically it is shown that the proposed Stackelberg decision approach maximizes the energy efficiency while still optimizing the throughput at the equilibrium.

We consider a non-cooperative Dynamic Spectrum Access (DSA) game where Secondary Users (SUs) access opportunistically the spectrum licensed for Primary Users (PUs). As SUs spend energy for sensing licensed channels, they may choose to be inactive during a given time slot in order to save energy. Then, there exists a tradeoff between large packet delay, partially due to collisions between SUs, and high-energy consumption spent for sensing the occupation of licensed channels. To overcome this problem, we take into account packet delay and energy consumption into our framework. Due to the partial spectrum sensing, we use a Partial Observable Stochastic Game (POSG) formalism, and we analyze the existence and some properties of the Nash equilibrium using a Linear Program (LP). We identify a paradox: when licensed channels are more occupied by PUs, this may improve the spectrum utilization by SUs. Based on this observation, we propose a Stackelberg formulation of our problem where the network manager may increase the occupation of licensed channels in order to improve the SUs' average throughput. We prove the existence of a Stackelberg equilibrium and we provide some simulations that validate our theoretical findings.

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. We consider Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy constraint as well as, the delay constraint, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We further consider that the secondary user (SU) may decide, at any moment, to use another dedicated way (3G) of communication in order to transmit its packets. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. In particular, numerical illustrations validate our theoretical findings. It is shown that optimal policy has a threshold structure.

We address a hierarchical game-theoretic model for studying power control in multi-carrier systems. The power control problem is modeled as a Stackelberg game where transmitters choose their control policy selfishly in order to maximize their individual energy efficiency. We establish the existence of the Stackelberg equilibrium and give its formal expression. Interestingly, we show that, under some assumptions, introducing a certain degree of hierarchy in a multi-carrier system results in a kind of coordination game where the primary and the secondary users have incentive to choose their transmitting carriers in such a way that they always transmit on orthogonal channels. Such an important result offers insights into how to design hierarchy in multi-carrier environments. Numerical results validate our theoretical claims.

Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Quality-centric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithms on a Microsoft Lync testbed. We observe that our algorithm improve significantly the QoE compared to standard AIMD congestion control mechanism.

In this work, we propose a new energy efficiency metric which allows one to optimize the performance of a wireless system through a novel power control mechanism. The proposed metric possesses two important features. First, it considers the whole power of the terminal and not just the radiated power. Second, it can account for the limited buffer memory of transmitters which store arriving packets as a queue and transmit them with a success rate that is determined by the transmit power and channel conditions. Remarkably, this metric is shown to have attractive properties such as quasi-concavity with respect to the transmit power and a unique maximum, allowing to derive an optimal power control scheme. Based on analytical and numerical results, the influence of the packet arrival rate, the size of the queue, and the constraints in terms of quality of service are studied. Simulations show that the proposed cross-layer approach of power control may lead to significant gains in terms of transmit power compared to a physical layer approach of green communications.

The authors are interested in evaluating the performance of a cognitive radio network composed of secondary and primary mobiles, and look for optimising the decision process of the secondary mobiles when they have to choose between licensed or unlicensed channels. In fact, the system is composed of several channels where only one unlicensed channel is shared between all the secondary mobiles, when they decide to use this particular channel. As the secondary mobiles are equipped with cognitive radios, they are able to sense the licensed channels and use one of them if it is free. The authors consider first the global system and look for the optimal proportion of secondary mobiles that sense the licensed channels in order to optimise an average performance of the system. Second, the authors assume that each secondary mobile decides to sense or not the licensed channels and, are interested in an equilibrium situation as the secondary mobiles are in competition. After showing the existence and the uniqueness of equilibrium, the performance of this equilibrium is evaluated by looking at the price of the anarchy of the system.

Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Quality-centric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithm on a Microsoft Lync testbed with unidirectional and bidirectional communications over a wireless network. We observe that for both scenarios, our algorithm improves significantly the QoE compared to standard AIMD congestion control mechanism.

This paper addresses the power control problem in wireless networks where transmitters choose their control policy freely and selfishly in order to maximize their individual energy-efficiency. In this framework, two new scenarios are studied in details: 1. a scenario where a fraction of the transmitters can observe the power levels of the other transmitters while the latter have no sensing capabilities; 2. a scenario where the observation structure is triangular, that is, the kthtransmitter can observe the k - 1thtransmitters (which corresponds to a multi-level hierarchical game). In both scenarios the equilibrium analysis (existence, uniqueness, determination, efficiency) is conducted. In scenario 1, it is proved that the game outcome Pareto dominates the one obtained when no transmitters can sense the others. Taking the sensing cost into account, a simple condition under which being a follower (namely a transmitter who senses) is better than a leader is provided. Interestingly, the existence of an optimum fraction of cognitive transmitters in terms of sum utility is proved in the case where the sensing cost is neglected. In scenario 2, it is proved analytically that knowing more leads to a better utility and the game outcome Pareto dominates the solution with no sensing. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in heterogeneous networks in terms of sensing capabilities.

In this paper, we are interested in evaluating the performance of a cognitive radio network. We look for optimizing the decision process of secondary mobiles between sensing or not primary's channels. We consider first the global system and look for the optimal proportion of secondary mobiles that sense the primary's channels. Second, we assume that each secondary mobile decides opportunistically to sense or not. In this case, the secondary mobiles are in competition. After showing the existence and uniqueness of the equilibrium, we evaluate the performance of this equilibrium by looking at the price of the anarchy.

We consider a noncooperative interaction among a large population of mobiles that interfere with each other through many local interactions. The first objective of this paper is to extend the evolutionary game framework to allow an arbitrary number of mobiles that are involved in a local interaction. We allow for interactions between mobiles that are not necessarily reciprocal. We study 1) multiple-access control in a slotted Aloha-based wireless network and 2) power control in wideband code-division multiple-access wireless networks. We define and characterize the equilibrium (called evolutionarily stable strategy) for these games and study the influence of wireless channels and pricing on the evolution of dynamics and the equilibrium.

In this work, we introduce hierarchy in wireless networks that can be modeled by a decentralized multiple access channel and for which energy-efficiency is the main performance index. In these networks users are free to choose their power control strategy to selfishly maximize their energy-efficiency. Specifically, we introduce hierarchy in two different ways: 1. Assuming single-user decoding at the receiver, we investigate a Stackelberg formulation of the game where one user is the leader whereas the other users are assumed to be able to react to the leader's decisions; 2. Assuming neither leader nor followers among the users, we introduce hierarchy by assuming successive interference cancellation at the receiver. It is shown that introducing a certain degree of hierarchy in non-cooperative power control games not only improves the individual energy efficiency of all the users but can also be a way of insuring the existence of a non-saturated equilibrium and reaching a desired trade-off between the global network performance at the equilibrium and the requested amount of signaling. In this respect, the way of measuring the global performance of an energy-efficient network is shown to be a critical issue.

A major contribution of biology to competitive decision making is the area of evolutionary games. It describes the evolution of sizes of large populations as a result of many local interactions, each involving a small number of randomly selected individuals. An individual plays only once; it plays in a one shot game against another randomly selected player with the goal of maximizing its utility (fitness) in that game. We introduce here a new more general type of games: a Stochastic Evolutionary Game where each player may be in different states; the player may be involved in several local interactions during his life time and his actions determine not only the utilities but also the transition probabilities and his life duration. This is used to study a large population of mobiles forming a sparse ad-hoc network, where mobiles compete with their neighbors on the access to a radio channel. We study the impact of the level of energy in the battery on the aggressiveness of the access policy of mobiles at equilibrium. We obtain properties of the ESS (Evolutionary Stable Strategy) equilibrium which, Unlike the Nash equilibrium concept, is robust against deviations of a whole positive fraction of the population. We further study dynamical properties of the system when it is not in equilibrium.

A decision maker (Actor) has to decide which of several available resources to use in the presence of an adversary (Controller) that can prevent the Actor of receiving information on the state of some of the resources. The Controller has a limitation on the amount of information it can conceal. We formulate this problem as a game and compute the most harmful behavior of the Controller and the best choice of a resource for the Actor. We identify cases in which the exact solution is computationally intractable, and provide approximate solutions with polynomial complexity.

Information dissemination is one of the most important aspects of routing mechanisms for network management. We study in this paper the problem of disseminating an information by a source trough all the nodes of a network. We consider an epidemic routing protocol in an Ad hoc network with a delayed feedback to the source. Indeed, the source controls the dissemination of the information in the network by sending an anti-packet, like a vaccine. We show that there exists a sufficient condition for stability of the information dissemination in the network depending on the spreading of information in the network and the delay of the feedback to the source.

In recent years, the notion of a service offering a degraded performance with respect to the best-effort service traditionally found in IP networks has gained acceptance among network researchers. Such a less-than-best-effort (LBE) service may he considered as another way of providing a differentiated quality of service, following A. Odlyzko's "damaged goods for the Internet" approach. In this paper we are interested in evaluating, from a pricing perspective, the implications of the two scheduling models commonly proposed for building a LBE service-namely, priority queueing and generalized processor sharing (GPS). In particular, we focus on the network operator's issue of maximizing her revenue. We wish to study, for each scheduler, how to set prices and, especially, the impact that a given queueing model may have on revenues when users are mostly sensitive to delay. Drawing on previous work by Mandjes (2003), we present analytical expressions of the revenue earned by the network operator, when a GPS scheduler is used. A comparison of optimal revenues shows that: (a) Priority Queueing is more efficient, in economic terms, than both a GPS scheduler and a simple FIFO queue, that is, a network with no service differentiation; (b) revenues are lower with a GPS scheduler than with a FIFO queue. These results may have implications both on the practical implementation of LBE services and on the Paris Metro Pricing proposal by Odlyzko (1999).