Liquidity providers (LPs) are essential figures in the operation of automated market makers (AMMs); in exchange for transaction fees, LPs lend the liquidity that allows AMMs to operate. While many prior works have studied the incentive structures of LPs in general, we currently lack a principled understanding of a special class of LPs known as Just-In-Time (JIT) LPs. These are strategic agents who momentarily supply liquidity for a single swap, in an attempt to extract disproportionately high fees relative to the remaining passive LPs. This paper provides the first formal, transaction-level model of JIT liquidity provision for a widespread class of AMMs known as Concentrated Liquidity Market Makers (CLMMs), as seen in Uniswap V3, for instance. We characterize the landscape of price impact and fee allocation in these systems, formulate and analyze a non-linear optimization problem faced by JIT LPs, and prove the existence of an optimal strategy. By fitting our optimal solution for JIT LPs to real-world CLMMs, we observe that in liquidity pools (particularly those with risky assets), there is a significant gap between observed and optimal JIT behavior. Existing JIT LPs often fail to account for price impact; doing so, we estimate they could increase earnings by up to 69% on average over small time windows. We also show that JIT liquidity, when deployed strategically, can improve market efficiency reducing slippage for traders, albeit at the cost of eroding passive LP profits by up to 44% per trade on average.

Federated Learning (FL) has gained significant attention in distributed machine learning by enabling collaborative model training across decentralized devices while preserving data privacy. Although extensive research has addressed statistical data heterogeneity, FL still faces several challenges, including high communication and computation overheads, energy inefficiency, and severe device heterogeneity, which require further investigation. Prior work has addressed these issues through sub-model training and partial parameter updates. However, such methods often suffer from inconsistent parameter distributions across clients, inaccurate global loss estimation, and increased bias and variance. In this paper, we propose FedPLT (Federated Learning with Partial Layer Training), a novel and structured partial training approach that divides neural network layers into equal-sized sub-layers and assigns them to clients based on their communication and computational capacities using a fixed assignment strategy. This ensures balanced parameter distribution, reduces variance, and supports varying model sizes across devices, making FedPLT well-suited for resource-constrained environments. In addition, we examine the performance of FedPLT when combined with optimal client sampling and show that this integration enhances federated learning performance by reducing sampling variance under the same communication constraints. Through extensive experiments, we show that FedPLT achieves performance comparable to, or even superior to, that of full-model training (i.e., FedAvg), while requiring significantly fewer trainable parameters per client. It also outperforms existing methods in the literature under highly heterogeneous systems by efficiently adapting to clients’ resource constraints and reducing the number of stragglers. Moreover, FedPLT can reduce the trained parameters by up to 65–85%, leading to substantial savings in communication costs, while maintaining the same level of performance as full-model training.

Federated learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. In practical scenarios, clients frequently engage in training multiple models concurrently, referred to as multi-model federated learning (MMFL). While concurrent training is generally faster than training one model at a time, MMFL exacerbates traditional FL challenges like the presence of non-i.i.d. data: since each individual client may only be able to train one model in each training round due to local resource limitations, the set of clients training each model will change in each round, introducing instability when clients have different data distributions. Existing single-model FL approaches leverage inherent client clustering to accelerate convergence in the presence of such data heterogeneity. However, since each MMFL model may train on a different dataset, extending these ideas to MMFL requires creating a unified cluster or group structure that supports all models while coordinating their training. In this paper, we present the first group-based client-model allocation scheme in MMFL able to accelerate the training process and improve MMFL performance. We also consider a more realistic scenario in which models and clients can dynamically join the system during training. Empirical studies in real-world datasets show that our MMFL algorithms outperform several baselines up to 15 %, particularly in more complex and statically heterogeneous scenarios.

Coflow scheduling and reducer placement are key to minimizing job completion times in data-parallel clusters. The RPC framework jointly addresses these tasks but assumes all coflows have equal importance, neglecting priority differentiation in practical workloads. This paper extends RPC by introducing a weighted scheduling mechanism that computes a score for each coflow based on its waiting time and priority, enabling priorityaware placement and bandwidth allocation. An efficient online algorithm minimizes these scores to favor high-priority coflows. Simulation results show that our approach significantly improves completion times for critical coflows and enhances overall fairness compared to baseline RPC.

In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server maintains a global model by aggregating the local models of the client devices. However, the repetitive communication between server and clients leaves room for attacks aimed at compromising the integrity of the global model, causing errors in its targeted predictions. In response to such threats on FL, various defense measures have been proposed in the literature [1]. In this paper, we present a powerful defense against malicious clients in FL, called FedSV, using the Shapley Value (SV), which has been proposed recently to measure user conribution in FL by computing the marginal increase of average accuracy of the model due to the addition of local data of a user. Our approach makes the identification of malicious clients more robust, since during the learning phase, it estimates the conribution of each client according to the different groups to which the target client belongs. FedSV's effectiveness is demonstrated by extensive experiments on MNIST datasets in a cross-silo context under various attacks.

Federated learning (FL) is a variant of distributed learning in which multiple clients collaborate to learn a global model without sharing their data with the central server. In real-world scenarios, a client may be involved in training multiple unrelated FL models, which we call multi-model federated learning (MMFL), and the client sampling strategy and task allocation are crucial for improving system performance. In this paper, we propose an optimal sampling method to minimize the variance of global updates for unbiased learning in MMFL systems. The resulting method achieves an average accuracy of over 30 % higher than other baseline methods, as we demonstrate through simulations on real-world federated datasets.

Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce$\mathtt {WDCoflow}$, a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a$\sigma$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to$3\times$more coflows that meet their deadline in comparison the best SoA solution, namely$\mathtt {CS\rm{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes,$\mathtt {WDCoflow}$is able to improve the admission per class up to$4\times$, while increasing the average weighted coflow admission rate.

With the advent of big data applications, coflow scheduling has become a cornerstone for the engineering of traffic in datacenters. Minimizing the average weighted Coflow Completion Times (CCT) is a crucial step to minimize the execution time of jobs running in distributed computing frameworks. In this paper, we present a new$\sigma $-order coflow scheduling solution, ONE-PARIS, an online semi-clairvoyant and semi-distributed implementation suitable to minimize the weighted CCT in production environments. We achieves this through ONE-PARIS scheduler for ordering coflows and a decentralized resource allocation mechanism, called Sync-Rate, enabling to respect the order of priority of coflows provided by ONE-PARIS and ensuring efficient synchronization between flows of the same coflow in order to free up bandwidth for low-priority flows. Extensive simulations on both synthetic and real traffics show that our proposed coflow scheduler outperforms other state-of-art schemes.

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the$\sigma$-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.

The Internet of Things (IoT) is changing today's world, and Machine Learning (ML) is a major contributor to this revolution in terms of data exchange to mature connected objects. In this context, federated learning (FL) is emerging, a new ML paradigm that drives a model on decentralized data, which can be distributed across many IoT devices. FL has grown considerably in recent years, both in academia and industry. However, the majority of FL algorithms assume that all client nodes are honest and willing to participate in cooperative model learning. Thus, each node is able to provide reliable local models to the central server. However, in real-life scenarios, nodes may be corrupt, malicious, or both, and may not cooperate fairly during training phases. In this paper, we address the above challenge by proposing a new FL algorithm called DistFL. The main objective of DistFL is to prevent biased training by identifying malicious nodes during the training phase. We evaluate the effectiveness of our technique and demonstrate it through a concrete implementation, comparing DistFL with conventional FL. Even with up to 50% malicious nodes, the runtime cost of the DistFL model is still better than that of the conventional FL model, and its final accuracy reaches 97% with a loss function convergence rate twice that of the conventional FL model. For this study, we used urban water data to deal with leakage and distribution faults in the water network among different end users in this area.

We present a game theoretic model that describes the competition between non-cooperating Service Providers (SPs) to purchase resources from the Infrastructure Provider (InP), and to offer paid services to their end-users. In the proposed model, the demand of a SP depends not only on its price and its quality of service (QoS), but also on the prices charged and QoS offered by its competitors. We consider that the InP owns the cloud facility to support the resources required by SPs to run the services of their end-users. When SPs access resources through virtually unlimited cloud capacity, resulting in competition to attract end-users with price required and QoS offered. We establish the existence and uniqueness of Nash equilibrium of the induced game. In addition, we design an online learning algorithm to allow SPs to reach the Nash equilibrium in a fully distributed fashion. Finally, we run simulations using Amazon EC2 instances to validate the proposed model’s consistency, investigate the impact of some parameters on the market price, and analyze the convergence of the equilibrium price learning algorithm.

This survey article explores recent advancements in data transfers, coflow scheduling, and reducer placement techniques for optimizing the performance of data-intensive applications in computer clusters. These techniques address the challenges of managing data transfers, optimizing resource allocation, and minimizing Coflow Completion Time (CCT). The surveyed research papers present innovative approaches such as intelligent data transfer scheduling, network-aware algorithms, near-optimal heuristics, and leveraging inter-flow relationships. These techniques aim to improve job completion time, enhance resource utilization, and minimize interference in datacenter networks. By summarizing these advancements, this survey article provides a comprehensive overview of the latest research in the field. The findings highlight the significance of these techniques in improving the performance and efficiency of data-intensive applications in computer clusters, and also identifies open challenges and future directions, stimulating further research and development in this area.

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.

Reducing Coflow Completion Time (CCT) has a significant impact on data-intensive application performance in datacenter networks. An efficient allocation of network resources allows for accelerating the computations to be performed. In this paper, we propose a new scheduler, named ELITE, to minimize the Weighted Coflow Completion Time (WCCT). Our scheduling algorithm is a 2-approximation of the optimal and the rate allocation can achieve a 4-approximation as long as the scheduling priority is respected. We also present a new rate allocation procedure, named RACO, that shows near-optimal performance when combined with our scheduling algorithms. We also propose a low complexity online scheduler, named LSPRT to attain near-optimal performance in online setting. With extensive simulations, we demonstrate the effectiveness of our algorithms by measuring the performance gain of ELITE and LSPRT over previous solutions in the literature. In particular, ELITE and LSPRT perform about 44 % better than Varys, while Sincronia achieves only 32 % against Varys.

Network slicing is becoming the platform of choice for several applications and services. Nowadays most applications are virtualized to gain flexibility and portability. With network slicing, operators can create multiple network slices or tenants, which can be used for certain applications with specific requirements. Behind the network slicing, a slice expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. Resource allocation decision encompasses a combination of different resource types (e.g., radio resource, CPU, memory, bandwidth). In this paper, we explore a differential pricing scheme that maximizes social welfare among slices as well as among end-users. To do so, we propose a pricing mechanism that makes fairness at multiple levels: fairness among slices and fairness among slice locations supported by each slice. Therefore, the proposed scheme is beneficial for both the slices and the end-users independent of their location. Additionally, we study the case where slices can manipulate their preferences to improve their utility. We show that the Fisher market game always has a pure Nash equilibrium and we prove Price of Anarchy is $\frac{1}{N}$, where N is the number of slices. Finally, we conduct simulations using Amazon EC2 instances to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model.

The growing demand for mobile data and the evolution of next-generation networks, particularly fifth-generation (5G) wireless networks, has called for new approaches to pricing and managing the limited capacity of existing network resources and infrastructures. In particular, emerging mobile applications like autonomous vehicles, augmented/virtual reality, and more broadly the Internet-of-Things will have heterogeneous demand patterns and service requirements, raising questions on how they should pay for their data usage and how next-generation networks can meet their demands with limited resources. Several recent policy changes and regulatory initiatives have been proposed to address the shift in demands due to next-generation networks and technologies. These include the FCC’s “5G Fast Plan,” which outlines strategies for modifying spectrum policies, infrastructure policies, and existing regulations, in light of emerging 5G technologies. This plan has included the rollback of net neutrality rules in June 2018, allowing broadband providers to offer a wider variety of service options.

International audience

Using millimeter wave (mmWave) bands in 5G self-organizing networks has a significant potential to provide high bandwidth. However, the major challenge lies in initial access beamforming where mmWave communications suffer from deafness problem that may cause a significant loss in the received power especially when narrow beams are adopted. This paper tackles the problem of beam alignment in mmWave 5G. The problem is formulated as a non-cooperative game between transmitter and receiver where each player tries to align its beamforming direction in a way to obtain maximum throughput. We first provide a full characterization of pure Nash equilibria. Then, we propose a gradient descent algorithm that allows users to learn their optimal beamwidth. Simulation results prove the performance of our beam alignment model since mmWave frequencies deliver narrow beams with high gain and significant capacity.

The demand for Internet services that require frequent updates through small messages, also known as microblogging, has tremendously grown in the past few years. Although the use of such applications by domestic users is usually free, their access from mobile devices is subject to fees and consumes energy from limited batteries. If a user activates his mobile device and is in the range of a publisher, an update is received at the expense of monetary and energy costs. Thus, users face a tradeoff between such costs and their messages aging. The goal of this paper is to show how to cope with such a tradeoff, by devising aging control policies. An aging control policy consists of deciding, based on the utility of the owned content, whether to activate the mobile device, and if so, which technology to use (WiFi or cellular). We present a model that yields the optimal aging control policy. Our model is based on a Markov Decision Process (MDP) in which states correspond to content ages. Using our model, we show the existence of an optimal strategy in the class of threshold strategies, wherein users activate their mobile devices if the age of their poadcasts surpasses a given threshold and remain inactive otherwise. The accuracy of our model is validated against traces from the UMass DieselNet bus network.

In this paper, we develop an analytical framework to compute the Quality-of-Experience (QoE) metrics of video streaming in wireless networks. Our framework takes into account the system dynamics that arises due to the arrival and departure of flows. We also consider the possibility of users abandoning the system on account of poor QoE. Considering the coexistence of multiple services such as video streaming and elastic flows, we use a Markov chain based analysis to compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and bitrate switching. Our simulation results validate the accuracy of our model and describe the impact of scheduler at eNB on the QoE metrics.

Dynamic Adaptive Streaming over HTTP (DASH) has become the standard choice for live events and on-demand video services. In fact, by performing bitrate adaptation at the client side, DASH operates to deliver the highest possible Quality of Experience (QoE) under given network conditions. In cellular networks, in particular, video streaming services are affected by mobility and cell load variation. In this context, DASH video clients continually adapt the streaming quality to cope with channel variability. However, since they operate in a greedy manner, adaptive video clients can overload cellular network resources, degrading the QoE of other users and suffer persistent bitrate oscillations. In this paper, we tackle this problem using a new eNB scheduler, named Shadow-Enforcer, which ensures minimal number of quality switches as well as efficient and fair utilization of network resources. Our scheduler works well under dynamic scenarios and mobility, and requires minimal information, i.e., just the set of video bitrates supported by DASH video clients. It consists of the cascade of a virtual scheduler, Shadow, and the actual scheduler, Enforcer, piloted by the virtual one. Extensive simulations demonstrate the efficiency, fairness and the smooth response to channel variations of the proposed solution.

Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of video streams, they neither coordinate with network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for users in the network. Motivated by this problem, we present D-VIEWS - a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to streaming clients and other network functions. Through simulations, we also study the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

High availability of containerized applications requires to perform robust storage of applications' state. Since basic replication techniques are extremely costly at scale, storage space requirements can be reduced by means of erasure and/or repairing codes. In this paper we address storage regeneration using repair codes, a robust distributed storage technique with no need to fully restore the whole state in case of failure. In fact, only the lost servers' content is replaced. To do so, new clean-slate storage units are made operational at a cost for activating new storage servers and a cost for the transfer of repair data. Our goal is to guarantee maximal availability of containers' state files by a given deadline. Upon a fault occurring at a subset of the storage servers, we aim at ensuring that they are repaired by a given deadline. We introduce a controlled fluid model and derive the optimal activation policy to replace servers under such correlated faults. The solution concept is the optimal control of regeneration via the Pontryagin minimum principle. We characterize feasibility conditions and we prove that the optimal policy is of threshold type. Numerical results describe how to apply the model for system dimensioning and show the tradeoff between activation of servers and communication cost.

HTTP Adaptive Streaming is able to dynamically match video quality to variable network conditions. This is a key feature for multimedia delivery when quality of service cannot be granted network-wide. For instance, the end-to-end throughput towards mobile terminals may suffer short term fluctuations due to fading. Hence, robust bitrate adaptation schemes become crucial in order to avoid degraded video reproduction. The objective, in this context, is to control the filling level of the playback buffer, maximize the video quality, and avoid unnecessary quality variations which may also impair the perceived quality of experience. In this work we study bitrate adaptation algorithms leveraging on Backward-Shifted Coding (BSC), a scalable video coding scheme able to cope with the effects of end-to-end throughput fluctuations. We have proposed a new adaptation scheme able to balance video rate smoothness and high network capacity utilization. Both the throughput-based and buffer-based variants of such scheme have been designed. Extensive simulations using synthetic and real-world video traffic traces show that the proposed solution performs remarkably well even under challenging network conditions.

The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users' QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization a hard task. This paper aims at taking a step further in order to address this limitation and meet users' profiles. Specifically, we propose a closed-loop control framework based on the users' (subjective) feedbacks to learn the QoE function and optimize it at the same time. Extensive simulation results show that the proposed scheme converges to a steady state, where the resulting QoE function noticeably improves the users' feedbacks.

The deployment of Wireless Sensor Networks to support optimal coverage is obviously a fastidious task, especially in hard to reach or inaccessible area. To address this problem, simplifying assumptions and simulations are commonly used techniques. However, these techniques may not be able to capture all the details about the problem to be solved. Hence, transition from the simulation to the actual deployment causes a loss of accuracy and robustness in coverage problem. In this paper, we address the above issue using two main ideas. First, pre-processing step with terrain analysis in three-dimensional environment. Second, a robust modeling approach based on both average and variance of visibility from each cell. We formulate the coverage optimization problem as a decision problem, where the objective is to maximize the area covered by a fixed number of sensors. Numerical results show that the proposed techniques are more efficient than the standard-based model, especially in hard to reach areas.

Cooperative sensing enables secondary users to combine individual sensing results in order to attain sensing accuracies beyond those achieved by consumer RF devices. However, due to sensing costs, secondary users may prefer not to cooperate to the sensing task, leading to higher false alarm probability. In this paper, we study how information about the presence of cooperators affects the dynamics of cooperative sensing schemes. We consider two scenarios, namely the case when SUs cannot detect the presence of other potential cooperators, and the case when SUs have prior information on the presence of other SUs in radio range. Using an evolutionary game framework, we demonstrate that protocols delivering such type of information to SUs reduce cooperation and ultimately lead to degraded network performance. Finally, a learning process based on the replicator dynamics is proposed which is capable to drive the system to the evolutionary stable solution. The results of the paper are illustrated through numerical simulations.

The surge of mobile data traffic forces network operators to cope with capacity shortage. The deployment of small cells in 5G networks shall increase radio access capacity. Mobile edge computing technologies can be used to manage dedicated cache memory at the edge of mobile networks. As a result, data traffic can be confined within the radio access network thus reducing latency, round-trip time and backhaul congestion. Such technique can be used to offer content providers premium connectivity services to enhance the quality of experience of their customers on the move. In this context, cache memory in the mobile edge network becomes a shared resource. We study a competitive caching scheme where contents are stored at a given price set by the mobile network operator. We first formulate a resource allocation problem for a tagged content provider seeking to minimize the expected missed cache rate. The optimal caching policy is derived accounting for popularity of contents, spatial distribution of small cells, and caching strategies of competing content providers. Next, we study a game among content providers in the form of a generalized non-smooth Kelly mechanism with bounded strategy sets and heterogeneous players. Existence and uniqueness of the Nash equilibrium are proved. Finally, numerical results validate and characterize the performance of the system.

In multi-rate IEEE 802.11 WLANs, the traditional user association based on the strongest received signal and the well-known anomaly of the MAC protocol can lead to overloaded access points (APs), and poor or heterogeneous performance. Our goal is to propose an alternative game-theoretic approach for association. We model the joint resource allocation and user association as a matching game with complementarities and peer effects consisting of selfish players solely interested in their individual throughputs. Using recent game-theoretic results, we first show that various resource sharing protocols actually fall in the scope of the set of stability-inducing resource allocation schemes. The game makes an extensive use of the Nash bargaining and some of its related properties that allow controlling the incentives of the players. We show that the proposed mechanism can greatly improve the efficiency of 802.11 with heterogeneous nodes and reduce the negative impact of peer effects such as its MAC anomaly. The mechanism can be implemented as a virtual connectivity management layer to achieve efficient APs-user associations without modification of the MAC layer.

A central problem in Delay Tolerant Networks (DTNs) is to persuade mobile nodes to participate in relaying messages. Indeed, the delivery of a message incurs a certain number of costs for a relay. We consider a two-hop DTN in which a source node, wanting to get its message across to the destination as fast as possible, promises each relay it meets a reward. This reward is the minimum amount that offsets the expected delivery cost, as estimated by the relay from the information given by the source (number of existing copies of the message, age of these copies). A reward is given only to the relay that is the first one to deliver the message to the destination. We show that under fairly weak assumptions, the expected reward the source pays remains the same irrespective of the information it conveys, provided that the type of information does not vary dynamically over time. On the other hand, the source can gain by adapting the information it conveys to a meeting relay. For the particular cases of two relays or exponentially distributed intercontact times, we give some structural results on the optimal adaptive policy.

As the demand for higher data rates is growing exponentially, homogeneous cellular networks have been facing limitations when handling data traffic. These limitations are related to the available spectrum and the capacity of the network. Heterogeneous networks (HetNets), composed of macro cells (MCs) and small cells (SCs), are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. Due to the large imbalance in transmit power between MCs and SCs in HetNets, intelligent user association (UA) is required to perform load balancing and to favor some SCs attraction against MCs. As long term evolution (LTE) cellular networks use the same frequency sub-bands, user equipment may experience strong intercell interference (ICI), especially at cell edge. Therefore, there is a need to coordinate the resource allocation (RA) among the cells and to minimize the ICI. In this paper, we propose a generic algorithm to optimize UA and RA in LTE networks. Our solution, based on game theory, permits to compute cell individual offset and a pattern of power transmission over frequency and time domain for each cell. Simulation results show significant benefits in the average throughput and also cell edge user throughput of 40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency.

Cooperative sensing is a promising technique that enables secondary users (SUs) to combine their channel observations in order to improve the spectrum sensing accuracy. However, in an adversarial environment where the secondary nodes are particularly selfish and the spectrum sensing is energy costly, selfish SUs can easily exploit the spectrum sensing result without participating in the sensing process. In this paper, we model the cooperative sensing as a volunteer dilemma where SUs have the choice to volunteer in order to sense and share the spectrum sensing results, or to free ride the spectrum sensing and achieve possibly a higher profitability. We mainly give a full characterization of the Nash equilibria and prove a counterintuitive property that claims: the probability of volunteering decreases when the number of SUs increases. Additionally, we propose a practical negotiation algorithm in order to select efficiently one SU to access to the channel. Finally, we propose a distributed algorithm that converges to Nash equilibria and show its performance through simulation results.

By leveraging geolocation and contextual information for mobile users, the prediction of the future throughput becomes more and more feasible. Many approaches on contextaware content delivery have been explored to balance the operators' limited resources with users' requirements. However, the perfect knowledge of the future context cannot be easily performed in real world, which represents a hurdle for most context-aware approaches. In this paper, we address a contextaware delivery algorithm for adaptive video streaming (NEWCAST) that have already been explored in [1] under perfect knowledge of future capacity, to balance the user's perception of the video and the cost of network usage. In order to make NEWCAST more resistant to eventual throughput prediction errors and adapt it to short-term horizons, we propose 4 algorithms that efficiently reduce the number of stalls by at least 75%.

Due to Orthogonal Frequency Division Multiple Access (OFDMA) mechanism adopted in LTE cellular networks, intra-cell interference is nearly absent. Yet, as these networks are designed for a frequency reuse factor of 1 to maximize the utilization of the licensed bandwidth, inter-cell interference coordination remains an important challenge. In both homogeneous and heterogeneous cellular networks, there is a need for scheduling coordination techniques to efficiently distribute the resources and mitigate inter-cell interference. In this paper, we propose a dynamic solution of inter-cell interference coordination performing an optimization of frequency sub-band reuse and transmission power in order to maximize the overall network utility. The proposed framework, based on game theory, permits to dynamically define frequency and transmission power patterns for each cell in the coordinated cluster. Simulation results show significant benefits in average throughput and also cell edge user throughput of 40% and 55% gains when performing the frequency sub-band muting and power control. Furthermore, we also obtain a meaningful improvement in energy efficiency.

Security is crucial for information systems. In a company, security management is traditionally controlled via a centralized single-point. However, when we deal with multiple computer systems interconnected in a wide area networks (WAN), the use of a central authority for security management is completely meaningless. In this paper, we propose a distributed decision-making designed to thwart viruses in a WAN. A key aspect is whether owners of devices are willing to update their anti-virus in order to protect their computers or not to pay for an anti-virus update and take the risk to be contaminated. Given the fact that computers are interconnected via networks and the Internet, the risk of being infected does not only depend on each computer's strategy, but also on the strategies chosen by other computers in the network. This makes the virus protection problem much more challenging. To do so, we model the interaction between nodes as a non-cooperative game in which each node decides individually whether to update the anti-virus or not. The virus spread is assumed to follow a biologically inspired epidemic model in which the dynamic of sources that disseminate the virus evolves as function of the popularity of virus using the influence linear threshold model. We first provide a full characterization of the equilibria of the game and then we investigate the impact of the update cost. In particular, we study the performance of the strategies at the equilibrium in terms of the update cost and the network size on both the security management system and the anti-virus producers. These results give some helpful insights on how secure is decentralizing antivirus update decisions.

Streaming services come with their own challenges and technical issues that still need to be addressed for satisfying the target quality of experience (QoE) of the end-users in mobile environments. In this paper, we explore the idea of combining users' context information with the packed prefetching process features to enhance users' QoE in heterogeneous networks. More specifically, we propose a scheduling mechanism for video streaming traffic, in which the access to the network resources is restricted to users with a signal-to-noise plus interference ratio (SINR) above a given threshold. This scheme benefits from the fact that, as users are in permanent motion, they may experience different SINR values during the same video streaming session offering the opportunity to only schedule users with good channel conditions. The proposed scheduling approach (subsequently referred to as context-aware mode switching (CAMS)) not only allows to achieve overall network spectral efficiency improvement, but also guarantees fairness and QoE among users. Our simulation results show that CAMS achieves almost 1 bit per second per Hertz gain compared to the conventional scheduler (without CAMS), and up to 87% improvement in the probability of no starvations when users move at 40 kmph.

The endeavor to develop 5G technology aims to support the recent outstanding mobile data traffic growth. In this regard, mobile network providers will be able to leverage on cloud edge-caching to offer services with enhanced quality of experience on the move. By this technology, dedicated cache space of mobile networks can be provisioned to OTT content providers, e.g., over metropolitan areas covered the network of a mobile network provider. In this work we address the problem of fair pricing such caching service, with storage the actual shared resource for caching. We study a scheme in which contents are dynamically stored in the edge memory. The mobile network provider offers a price λ for storing contents on the shared cache, thus engendering competition for cache memory sharing among content providers. We model such competition among OTT content providers using the economic notion of Kelly mechanism. Hence, we have studied the Stackelberg equilibrium, i.e., the optimal price configuration for the network provider. Numerical results describe the structure of the Nash equilibrium and the optimal prices resulting from the network provider optimal strategy.

The knowledge of the future capacity variations in wireless networks using smartphones becomes more and more possible by exploiting the rich contextual information from smartphone sensors through mobile applications and services. It is entirely likely that such contextual information, which may include the traffic, mobility and radio conditions, could lead to a novel agile resource management not yet thought of. Inspired by the attractive features and potential advantages of this agile resource management, several approaches have been proposed during the last period. However, agile resource management also comes with its own challenges, and there are significant technical issues that still need to be addressed for successful rollout and operation of this technique. In this paper, we propose an approach (called NEWCAST) for anticipating throughput variation for mobile video streaming services. The solution of the optimization problem realizes a fundamental trade-off among critical metrics that impact the user's perceptual quality of the experience (QoE) and system utilization. Both simulated and real-world traces collected from [1] are carried out to evaluate the performance of NEWCAST. In particular, it is shown that NEWCAST provides the efficiency, computational complexity and robustness that the new 5G architectures require.

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of initial delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We model the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for constant bit-rate (CBR) streaming. The explicit form starvation probabilities and mean start-up delay are obtained by use of a matrix function approach. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of variable bit-rate (VBR) streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior in practice.

Real-time applications over wireless networks often suffers from jitter, delay and packet loss. The traditional techniques for error control are FEC (Forward Error Correction) and ARQ (Automatic Repeat Request). In this paper a hybrid FEC/ARQ mechanisms with limiting the slots number between the loss of the last retransmission of the original block n and the reception of block n + φ is analyzed in an environment of real time applications. We assume that the packet size increases when adding redundancy so that the amount of useful information contained in a packet is unchanged. We evalute analytically the performance of TFRC (TCP Friendly Rate Control) and ARC (Analytical Rate Control Protocol) as a function of the parameters of the FEC scheme, the limit number of retransmission used by ARQ scheme and the number of slots threshold Sth. We show that the combination of FEC and ARQ with limiting the Sth reduces the number of retransmissions and keeps a reasonable delay for real time traffic in wireless networks.

In this paper we introduce a novel framework for the distributed control of DTNs in order to support message replication the devices acting as relays need to sacrifice part of their batteries. Due to the fact that feedback messages in DTNs cannot be used, the knowledge of the delivery status is unknown for the source. We model the problem as a Partially Observable Markov Decision Process (POMDP) and we derive an optimal policy for forwarding messages in the network; in fact the source node uses a belief to decide either to continue forwarding a message or not. We provide extensive numerical results to validate the proposed scheme in which our mechanism exchanges less messages than epidemic routing, hence, less energy is consumed.

The goal of this paper is to study the behaviour of viewcount in YouTube. We first propose several bio-inspired models for the evolution of the viewcount of YouTube videos. We show, using a large set of empirical data, that the viewcount for 90% of videos in YouTube can indeed be associated to at least one of these models, with a Mean Error which does not exceed 5%. We derive automatic ways of classifying the viewcount curve into one of these models and of extracting the most suitable parameters of the model. We study empirically the impact of videos' popularity and category on the evolution of its viewcount. We finally use the above classification along with the automatic parameters extraction in order to predict the evolution of videos' viewcount.

A central problem in Delay Tolerant Networks (DTNs) is to persuade mobile nodes to participate in relaying messages. Indeed, the delivery of a message incurs a certain number of costs for a relay. We consider a two-hop DTN in which a source node, wanting to get its message across to the destination as fast as possible, promises each relay it meets a reward. This reward is the minimum amount that offsets the expected delivery cost, as estimated by the relay from the information given by the source (number of existing copies of the message, age of these copies). A reward is given only to the relay that is the first one to deliver the message to the destination. For two relays and exponentially distributed inter-contact times, we show that the expected reward the source pays remains the same irrespective of the information it conveys, provided that the type of information does not vary dynamically over time. On the other hand, the source can gain by adapting the information that it conveys to a meeting relay.

Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as a FIFO queue with exponential service time and Poisson arrivals. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches to obtain exact distribution of the number of buffer starvations, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using Tàkacs Ballot theorem. The recursive approach, though not offering an explicit result, allows us to obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.

We consider the problem of “fair” scheduling the resources to one of the many mobile stations by a centrally controlled base station (BS). The BS is the only entity taking decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric α-fair scheduling problems from a game-theoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash equilibrium (NE) that we call a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise nonrobust (in presence of noncooperation) α-fair scheduling algorithms.

Social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy. A new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted such behavior exceeds some threshold. In this paper we study the question of whether the emergence of threshold policies may be modeled as a result of some rational process which would describe the behavior of non-cooperative rational members of some social network. We focus on situations in which individuals take the decision whether to access or not some content, based on the number of views that the content has. Our analysis aims at understanding not only the behavior of individuals, but also the way in which information about the quality of a given content can be deduced from view counts when only part of the viewers that access the content are informed about its quality. In this paper we present a game formulation for the behavior of individuals using a meanfield model: the number of individuals is approximated by a continuum of atomless players and for which the Wardrop equilibrium is the solution concept. We derive conditions on the problem's parameters that result indeed in the emergence of threshold equilibria policies. But we also identify some parameters in which other structures are obtained for the equilibrium behavior of individuals.

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs). Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station in the presence of fast fading. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the average throughput of opportunistic scheduling, while the variance of throughput has very limited impact on starvation behavior.

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.

Our purpose in this paper is to obtain the exact distribution of the number of buffer starvations within a sequence of N consecutive packet arrivals. The buffer is modeled as an M/M/1 queue. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit solution, but at the cost of the high complexity order in certain circumstances. The recursive approach, though not offering an explicit result, needs fewer computations. We further propose a fluid analysis of starvation probability on the file level, given the distribution of file size and the traffic intensity. The starvation probabilities of this paper have many potential applications. We apply them to optimize the quality of experience (QoE) of media streaming service, by exploiting the tradeoff between the start-up delay and the starvation.

We study revenue-maximizing in a wireless system where interested users share a common spectrum and interfere with each other. Capacity is increased in proportion of the number of users. Our objective is to design a scheme that achieves an optimal solution for the provider with respect to users strategy, bandwidth management and some fairness criteria. We consider a flat rate pricing for users. Two cases are studied: firstly, an approximation to the original problem when the provider has a perfect knowledge about the channel state of each user is proposed. In the second case, a decision theoretic approach based on a POMDP framework is elaborated when the provider has only a partial information on the system state. Additionally, we provide numerical results that illustrate the performance of the proposed solutions.

This paper presents a cross-layered framework to study linear and circular ad hoc networks built on the IEEE 802.11e Enhanced Distributed Coordination Function. We investigate the intricate interactions among PHY, MAC and Network layers. Now, the carrier sense threshold, the transmit power, the contention window size, the retransmissions retry limit and the multi rates are jointly incorporated. We propose an equivalent network where each node is able to listen to other nodes, i.e., the effect of hidden nodes could be definitely eliminated. Then, we develop an analytical model that predicts the throughput of each connection as well as the stability of forwarding queues at intermediate nodes while each node is able to prevent the accumulative interference phenomenon. Further, performance of such a system is evaluated via simulation. We show that the performance measures of MAC layer are affected by the traffic intensity of flows to be forwarded and the average hop length. Moreover, attempt rate and collision probability are dependent on the traffic flows, PHY parameters and routing scheme.

A central scheduling problem in wireless communications is that of allocating resources to one of many mobile stations that have a common radio channel. Much attention has been given to the design of efficient and fair scheduling schemes that are centrally controlled by a base station (BS) whose decisions depend on the channel conditions reported by each mobile. The BS is the only entity taking decisions in this framework. The decisions are based on the reports of mobiles on their radio channel conditions. In this paper, we study the scheduling problem from a game-theoretic perspective in which some of the mobiles may be noncooperative or strategic, and may not necessarily report their true channel conditions. We model this situation as a signaling game and study its equilibria. We demonstrate that the only Perfect Bayesian Equilibria (PBE) of the signaling game are of the babbling type: the noncooperative mobiles send signals independent of their channel states, the BS simply ignores them, and allocates channels based only on the prior information on the channel statistics. We then propose various approaches to enforce truthful signaling of the radio channel conditions: a pricing approach, an approach based on some knowledge of the mobiles' policies, and an approach that replaces this knowledge by a stochastic approximations approach that combines estimation and control. We further identify other equilibria that involve non-truthful signaling.

Most theoretical research on routing games in telecommunication networks has so far dealt with reciprocal congestion effects between routed entities. Yet in networks that support differentiation between flows, the congestion experienced by a packet depends on its priority level. Another differentiation is made by compressing the packets in the low priority flow while leaving the high priority flow intact. In this paper we study such kind of routing scenarios for the case of non-atomic users and we establish conditions for the existence and uniqueness of equilibrium.

We consider the problem of centrally controlled 'fair' scheduling of resources to one of the many mobile stations connected to a base station (BS). The BS is the only entity making decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric α-fair scheduling problems from a game-theoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash Equilibrium (NE) called a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise non-robust (in presence of noncooperation) α-fair scheduling algorithms.

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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.

The Adaptive Modulation and Coding (AMC) scheme which handles user’s mobility issue plays a significant role to improve the desired quality of service in High Speed Downlink Packet Access (HSDPA) networks. We develop a resource allocation which maintains constant bit rate for real-time (RT) and non-

In this paper we study the capacity of the OFDMA- based IEEE802.16 WiMAX network in the presence of two types of traffic: real-time and non-real-time, including adaptive modulation and coding (AMC) with user intra cell mobility. Our work deals with the performance of the connexion admission and resource allocation algorithm over a general ranging arrival process: it has been recently proved that the exponential distribution is inappropriate. Based on the generalized traffic processes, we develop a resource allocation that maintains the bit rate of real time connections independently of the user position in the cell. Moreover, we enhance the CAC algorithm with a mecanism designed for the mobility management. Using a novel discrete time Markovian analysis we evaluate the impact of our resource allocation for mobility behavior on the RT and NRT connections as the blocking and dropping probabilities, mean sojourn time and throughput.

We consider evolving games with finite number of players, in which each player interacts with other randomly selected players. The types and actions of each player in an interaction together determine the instantaneous payoff for all involved players. They also determine the rate of transition between type-actions. We provide a rigorous derivation of the asymptotic behavior of this system as the size of the population grows. We show that the large population asymptotic of the microscopic model is equivalent to a macroscopic evolutionary game in which a local interaction is described by a single player against an evolving population profile. We derive various classes of evolutionary game dynamics. We apply these results to spatial random access games in wireless networks.

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 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.

The capacity of CDMA wireless network is usually studied considering two classes of services: real-time and best-effort. In this paper, we are interested in analyzing sharing between three classes of services: real-time (RT), non-real-time (NRT) and best-effort (BE). A classical approach which is widely used in wireless networks is based on adaptively deciding how many channels to allocate to calls of a given class. The rational behind our idea is that the NRT class (e.g. FTP) requires a minimum transmission rate. The capacity allocated to NRT traffic includes a fixed portion of bandwidth as well as a dynamic part which is shared with RT service. In contrast, BE applications can adapt their transmission rate to the network’s available resources. Hence, the best-effort applications can use only the unused resources of the NRT band. Using a spectral analysis approach, we compute the steady-state distribution of the calls number for those different classes which allows us to provide explicitly the performance measure. The QoS parameters of interest are primarily the blocking probability for both RT calls and NRT calls, and expected sojourn times for both NRT calls and BE calls. We finally provide numerical study to show the benefit of our capacity allocation method by providing a desired quality level of service for NRT services, and we propose some CAC policies for NRT and BE services.

In this paper, we consider a multimedia data transmission system over a wireless channel, where packets are queued at the transmitter. Multimedia data transmission over wireless networks often suffers from delay, jitter and packet loss. The main problem to implement a wireless network is the high and variable bit error rate in the radio link (fading, shadowing etc), making it necessary to use an additional error control mechanism. Among the most important performance measures for real time applications are the packet loss probability and expected delay. In order to improve the radio link, one often retransmits packets that have not been well received (using the Automatic Retransmission reQuest - ARQ). This however may lead to queuing phenomena and increased delay due to retransmissions, and to losses of packets due to buffer overflow. In this paper, we assume that the number of retransmission is finite. We present a queuing analysis which based on matrix-geometric solutions in M/G/1 type Markov chains, in order to compute some performance measures of interest.

In this paper, we propose and analyse a new scheme to handle ranging requests in IEEE 802.16e networks based on differentiated backoff parameters and a novel hierarchical code partitioning principle. Our analysis is of the form of a fixed-point equation which characterizes the system operating points and which allows us to compute the attempt rate of each QoS class as well as the distribution of the number of ranging bandwidth requests received successfully by the base station as well as their average delay. Both analysis and simulation confirm that our scheme yields high throughput and low collision probability for real-time connections without altering the collision probability for non-real-time connections.

We consider the situation where N nodes share a common access point. With each node i there is an associated buffer and channel state that change in time. Node i dynamically chooses both the power and the admission control to be adopted so as to maximize the expected capacity, which depends on the actions and states of all the players, given its power and delay constraints. The information structure that we consider is such that each player knows the state of its own buffer and channel and its own actions. It does not know the states of, and the actions taken by other players. Using Markov Decision Processes we analyze the single player optimal policies under different model parameters. In the context of stochastic games we study the equilibria of the N player scenario.

We analyze IEEE 802.16e medium access control (MAC) sublayer and provides a simple analytical model to compute the 802.16e MAC throughput. Our approach is to begin with a key approximation made in IEEE 802.11. This study leads to a fixed point equation, which characterizes the operating point of system. It allows us to compute the attempt rate of a mobile in saturated case as well as the throughput formulas for the overall network. Moreover, our performance model deals with a recently released IEEE802.16 criterium : the tr parameter, which have a main impact on the MAC performance.

A major contribution of biology to competitive decision making is the development of the discipline of evolutionary games. Its ESS (evolutionary stable strategy) equilibrium concept, well adapted to large populations of players, describes robustness against deviations of a whole fraction of the population (in contrast to Nash equilibrium that requires robustness against a single user's deviation). The second appealing element of evolutionary games, the replicator dynamics, describes the evolution of strategies in time. Under suitable conditions, this (and some other) dynamics, converge to an ESS. In this paper we study the effect of slow time scale delays on the convergence of various dynamics. We apply this to an evolutionary game describing competition between mobile terminals over the access to a common channel.

This paper studies distributed choice of retransmission probabilities in slotted ALOHA. Both the cooperative team problem as well as the noncooperative game problem is considered. In previous work that has focused on the maximization of throughput, it was shown that in heavy load, this maximization is obtained at the cost of a huge delay of backlogged packets. This motivates us to investigate the delay minimization problem as well as the multicriterion problem of minimizing the average expected delay (or maximizing the throughput) subject to constraints on the expected delay of backlogged packets. A Markov chain analysis is used to obtain optimal and equilibrium retransmission probabilities and expected delays analysis.