Network slicing, a key feature of next-generation mobile networks, has underexplored economic implications. We model a Stackelberg game where a mobile operator sells a slice to a service provider offering two service tiers, showing that slicing boosts revenues and improves resource allocation.

Network slicing is a key component of 5G-and-beyond networks but induces many questions related to an associated business model and its need to be regulated due to its difficult co-existence with the network neutrality debate. We propose in this paper a slicing model in the case of heterogeneous users/applications where a service provider may purchase a slice in a wireless network and offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. We obtain thanks to game theory the economically-optimal slice size and prices charged by all actors. We also compare with the case of a unique “pipe” (no premium service) corresponding to a fully-neutral scenario and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario and the eventual need for regulation.

This paper deals with the (still) hot and sensitive network neutrality debate. It designs a model simultaneously encompassing several scenarios regarding the content-providing market structure: a monopolistic content provider, competitive ones, and a vertically-integrated one facing a non vertically-integrated competitor. We each time compare the outputs when non-neutral, weak neutral or strong neutral policies are applied, in order to get insights regarding the possible consequences of each type of neutrality rule to determine if regulation is recommended. We show from our data set that, among other notable results, there is no interest in imposing neutrality since the ISP choice leads to the situation benefiting to society. Furthermore, the optimal scenario for society is when there is vertical integration, in which case the integrated federation actually opts for neutrality.

Network slicing is a key component of 5G-and-beyond networks, requiring to define a business model for resource allocation. We consider a model with a Service Provider (SP) that may purchase a slice in a wireless network, in order to offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. One optimization problem for the SP is the choice of how much resource to allocate to that slice. We also compare with the case of a unique “pipe” (no premium service) and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario.

In current sensor-based monitoring solutions, each application involves a specified deployment and requires significant configuration efforts to adapt to changes in the sensor field. In this paper, we propose a generic solution that relies on the massive deployment of battery-powered sensors. More precisely, we present a solution for LPWAN sensors emissions scheduling to ensure overall regular sensor data emissions over time (at a rate chosen by the user) while limiting management costs incurred by sensors’ arrivals and departure. Our objectives include monitoring quality that we evaluate through a "diversity" metric encompassing that information value depletes with time, plus management cost quantified by the number of orders sent to sensors. Modeling arrivals and departures as random processes, we compute those performance metrics as functions of the overall data reception period selected and evaluate them against alternative scheduling methods. We show that our solution is better suited for Massive IoT contexts.

In today’s monitoring solutions, each application involves custom deployment and requires significant configuration efforts to accommodate sensor changes. In contrast, in this paper, we consider a massive deployment of battery-powered sensors to propose a more versatile monitoring solution that is not tied to the physical deployment of devices.First, we define a framework for the definition of a monitoring strategy, for which we propose a generic monitoring accuracy metric, which, weighted to the lifetime of the monitoring network, allows the characterization of a multi-objective problem.We then introduce a specific two-parameter instantiation for the period update function, that ensures strictly periodic emissions from sensors even when new sensors join the system over time. We show through simulations how the two parameters– target emission period and number of jointly used sensors–can be chosen according to the objectives for the monitoring, by highlighting the Pareto front for accuracy and energy-efficiency.1

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

The high data rates and diversity of services in 5G require a flexible and efficient use of all the available frequencies. In 5G networks, new approaches of dynamic spectrum sharing will be deployed, allowing Mobile Network Operators (MNOs) to access other incumbents' spectrum, after obtaining a license from the regulator. The attribution of licenses will be made via auction mechanisms valid for given geographical areas and durations. To determine how to bid, each MNO has to estimate his valuation for spectrum i.e., how much he is willing to pay for spectrum. In this paper, we propose a model for estimating that valuation. The model is based on Markov chain modeling of user behavior, to compute the MNO satisfaction as a function of the obtained spectrum. We then illustrate our method by applying it to real operator data.

Internet of Things (IoT) faces multiple challenges to achieve high reliability, low-latency and low power consumption. Its performance is affected by many factors such as external interference coming from other coexisting wireless communication technologies that are sharing the same spectrum. To address this problem, we introduce a general approach for the identification of poor-link quality channels. We formulate our problem as a multi-player multi-armed bandit problem, where the devices in an IoT network are the players, and the arms are the radio channels. For a realistic formulation, we do not assume that sensing information is available or that the number of players is below the number of arms. We develop and analyze a collaborative decentralized algorithm that aims to find a set of $m$ $(\epsilon,m)$-optimal arms using an Explore-$m$ algorithm (as denoted by Kalyanakrishnan and Stone (2010)) as a subroutine, and hence blacklisting the suboptimal arms in order to improve the QoS of IoT networks while reducing their energy consumption. We prove analytically and experimentally that our algorithm outperforms selfish algorithms in terms of sample complexity with a low communication cost, and that although playing a smaller set of arms increases the collision rate, playing the optimal arms only improves the QoS of the network.

Network neutrality has been the subject of a hot debate worldwide. Regulation has been implemented in many countries to enforce the principle of a neutral network. But compliance to the rules has to be checked thanks to specific measurement tools. This paper aims at highlighting the weaknesses of current network neutrality measurement tools and at providing hints on challenges to be addressed on the topic.

Sponsored data, where content providers have the possibility to pay wireless providers for the data consumed by customers and therefore to exclude it from the data cap, is getting widespread in many countries, but is forbidden in others for concerns of infringing the network neutrality principles. We present in this paper a game-theoretic model analyzing the consequences of sponsored data in presence of competing wireless providers, where sponsoring decided by the content provider can be different at each provider. We also discuss the impact on the proportion of advertising on the displayed content. We show that, surprisingly, the possibility of sponsored data may actually reduce the benefits of content providers and on the other hand increase the revenue of ISPs in competition, with a very limited impact on user welfare.

LoRaWAN is a recently proposed MAC layer protocol which manages communications between LoRa-based gate-ways and end-devices. It has attracted much scientific attention due its physical layer characteristics, but mainly due to its versatile configuration parameters. However, it is known that LoRaWAN-based transmissions suffer from extensive collisions due to the unregulated access to the medium. For this reason, various techniques that alleviate the burst of collisions have been proposed in the literature. In this paper, we deal with the problem of fast data delivery in LoRa-based networks. We model a network where transmissions follow a Poisson process. We compute the average packet success probability per Spreading Factor (SF) assuming orthogonal transmissions. We, then, formulate an SF optimization problem to maximize the success probability given an amount of data per node and a maximum data collection time window. We show - both theoretically and using simulations - that the overall success probability can be improved by approximately 100% using optimal SF assignments. We validate our findings using a 10-node testbed and extensive experiments. Despite that experiments reveal the existence of inter-SF interference, our solution still provides the best performance compared to other LoRaWAN configurations.

In order to track the users who illegally re-stream live video streams, one solution is to embed identified watermark sequences in the video segments to distinguish the users. However, since all types of watermarked segments should be prepared, the existing solutions require an extra cost of bandwidth for delivery (at least multiplying by two the required bandwidth). In this paper, we study how to reduce the inner delivery (traffic) cost of a Content Delivery Network (CDN). We propose a mechanism that reduces the number of watermarked segments that need to be encoded and delivered. We calculate the best- and worst-case traffics for two different cases: multicast and unicast. The results illustrate that even in the worst cases, the traffic with our approach is much lower than without reducing. Moreover, the watermarked sequences can still maintain uniqueness for each user. Experiments based on a real database are carried out, and illustrate that our mechanism significantly reduces traffic with respect to the current CDN practice.

The Industrial Internet of Things (IIoT) faces multiple challenges to achieve high reliability, low-latency and low power consumption. The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol aims to address these issues by using frequency hopping to improve the transmission quality when coping with low-quality channels. However, an optimized transmission system should also try to favor the use of high-quality channels, which are unknown a priori. Hence reinforcement learning algorithms could be useful.

Licensed Shared Access (LSA) is a new complementary regulatory framework which aims to optimize wireless spectrum utilization. Under LSA, an incumbent can share his owned spectrum through an “LSA licensee”. This sharing is supervised by the regulator, but the allocation and pricing mechanism to be used is not specified yet. To fill that gap, in this paper we propose PAM: Proportional Allocation Mechanism, which is a truthful auction mechanism offering a good compromise between fairness and efficiency and can generate the highest revenue to the regulator compared to other truthful mechanisms proposed in the literature.

In October 2015, the European parliament has decided to forbid roaming charges among EU mobile phone users, starting June 2017, as a first step toward the unification of the European digital market. In this paper, we aim at investigating the consequences of such a measure from an economic perspective. In particular, we analyze the effect of the willingness-to-pay heterogeneity among users (also due to wealth heterogeneity), and the fact that the roaming behavior is positively correlated with wealth. Considering a monopolistic operator, we compare the paid-roaming situation (with usage-based pricing) to the envisioned free-roaming from the point of view of the operator and of users. Our analysis suggests that imposing free roaming degrades the revenues of the operator but can also deter some users from subscribing. This is because paid roaming allows some partial market segmentation; hence we conclude that such (apparently beneficial) regulatory decisions must be taken with care.

To address the problem of illegal re-streaming of video streams, existing solutions are based on watermarking the legal video to track the leak users who re-stream the stream on illegal platform. However, these solutions do not aim at tracking leaks as fast as possible, nor are adaptive to the number of users. We present a CDN-based adaptive delivery architecture for watermarked streaming. We propose an algorithm to generate unique sequences of watermarks for the legal delivery. This algorithm is adaptive in the number of users and optimal for the time needed to detect the leak. It meets the demand of live video providers who do not know in advance the number of clients for a stream. Our algorithm copes with thousands of new clients per seconds and enables leak detection in less than five minutes with only five watermarks for live video streams watched by one billion of clients.

Content delivery networks (CDNs) have become key telecommunication actors. They contribute to improve significantly the quality of services delivering content to end users. However, their impact on the ecosystem raises concerns about their “fairness,” and therefore the question of their inclusion in the neutrality debates, becomes relevant. This paper aims at analyzing the impact of a revenue-maximizing CDN on some other major actors, namely, the end-users, the network operators, and the content providers, at comparing the outcome with that of a fair behavior, and at providing tools to investigate whether some regulation should be introduced. We present a mathematical model and show that there exists a unique optimal revenue-maximizing policy for a CDN actor, in terms of dimensioning and allocation of its storage capacity, and depending on parameters such as prices for service/transport/storage. Numerical experiments are then performed with both synthetic data and real traces obtained from a major video-on-demand provider. In addition, using the real traces, we compare the revenue-based policy with policies based on several fairness criteria.

Primary User Emulation Attack (PUEA), in which attackers emulate primary user signals causing restriction of secondary access on the attacked channels, is a serious security problem in Cognitive Radio Networks (CRNs). A user performing a PUEA for selfishly occupying more channels is called a selfish PUEA attacker. Network managers could adopt a surveillance process on disallowed channels for identifying illegal channel occupation of selfish PUEA attackers and hence mitigating selfish PUEA. Determining surveillance strategies, particularly in multichannel context, is necessary for ensuring network operation fairness. In this paper, we formulate a game, called multi-channel surveillance game, between the selfish attack and the surveillance process in multi-channel CRNs. The sequence-form representation method is adopted to determine the Nash Equilibrium (NE) of the game. We show that performing the obtained NE surveillance strategy significantly mitigates selfish PUEA.

Content Delivery Network (CDN) services largely affect the delivery quality perceived by users. While those services were initially offered by independent entities, some large ISP now develop their own CDN activities to control costs and delivery quality. But this new activity is also a new source of revenues for those vertically integrated ISP-CDNs, which can sell those services to content providers. In this paper, we investigate the impact of having an ISP and a vertically-integrated CDN, on the main actors of the ecosystem (users, competing ISPs). Our approach is based on an economic model of revenues and costs, and a multilevel game-theoretic formulation of the interactions among actors. Our model incorporates the possibility for the vertically-integrated ISP to partially offer CDN services to competitors in order to optimize the trade-off between CDN revenue (if fully offered) and competitive advantage on subscriptions at the ISP level (if not offered to competitors). Our results highlight two counterintuitive phenomena: an ISP may prefer an independent CDN over controlling (integrating) a CDN, and from the user point of view vertical integration is preferable to an independent CDN or a no-CDN configuration. Hence, a regulator may want to elicit such CDN-ISP vertical integrations rather than prevent them.

Electric vehicles (EVs), as their penetration increases, do not only challenge the sustainability of the power grid but also stimulate and promote its upgrading. Indeed, EVs can actively reinforce the development of the smart grid if their charging processes are properly coordinated through two-way communications, possibly benefiting all types of actors. Because grid systems involve a large number of actors with nonaligned objectives, we focus on the economic and incentive aspects, where each actor behaves in its own interest. We indeed believe that the market structure will directly impact the actors' behaviors, and as a result, the total benefits that the presence of EVs can earn in the society, hence the need for a careful design. This survey provides an overview of economic models considering unidirectional energy flows and bidirectional energy flows, i.e., with EVs temporarily providing energy to the grid. We describe and compare the main approaches, summarize the requirements on the supporting communication systems, and propose a classification to highlight the most important results and lacks.

We investigate the impact of decisions made by a CDN willing to maximize its revenue through the management of cache servers. Based on a model with two network providers, we highlight that revenue-oriented management policies can affect the user-perceived quality of experience, impacting the competition among network access providers in favor of the largest one. Since this contradicts the principle underpinning network neutrality-although not with the technical net neutrality rules-we discuss the necessity to regulate CDN activity.

Vehicular networks, besides supporting safety-oriented applications, are nowadays expected to provide effective communication infrastructure also for supporting leisure-oriented application including content sharing, gaming and Internet access on the move. This work focuses on Vehicle to Infrastructure (V2I) scenarios, where multiple content providers own a physical infrastructure of Road Side Units (RSUs) which they use to sell contents to moving vehicles. Content provider/RSU owners compete by adapting their pricing strategies with the selfish objective to maximize their own revenues. We study the economics of the price competition between the providers by resorting to game theoretic tools. Namely, we formalize a simultaneous price game among the operators further studying the existence of Nash equilibria and their related quality in terms of Price of Anarchy and Price of Stability. The proposed game model is finally used to assess the impact onto the game equilibra of several practical factors including the vehicles' willingness to pay, the traffic densities, and the configuration of the physical networks of RSUs.

The network reconfiguration (NR) techniques are key strategies to reduce the energy consumption in cellular networks. In this paper we propose a mechanism to further increase the energy gains resulting of the application of these strategies. Our approach exploits the delay tolerance of the users to increase the periods when no reconfiguration is needed. We developed the analytical model of the mechanism dynamics and we evaluated it numerically for two NR strategies. The results show up to 13 percentage points of performance improvement regarding the traditional NR strategies for a maximal delay of 50s.

The search neutrality debate questions the ranking methods of search engines. We analyze the issue when content providers offer content for free, but get revenues from advertising. We investigate the noncooperative game among competing content providers under different ranking policies. When the search engine is not involved with high-quality content providers, it should adopt neutral ranking, also maximizing user quality-of-experience. If the search engine controls high-quality content, favoring its ranking and adding advertisement yield a larger revenue. Though user perceived quality may not be impaired, the advertising revenues of the other content providers drastically decrease.

Almost all modern mobile devices are equipped with a number of various wireless interfaces simultaneously, so that each user is free to select between several types of wireless networks. This opportunity raises a number of challenges, since in general selfish choices do not lead to a globally efficient repartition of users over networks. The most popular approach in this context is to charge an extra tax for connecting to overloaded networks, thus incentivizing users to choose less congested alternatives. In this paper we apply that idea to a system where several networks with a common coverage area coexist. Moreover we assume that users -or the applications they use- are heterogeneous in their sensitivity to the congestion-varying Quality-of-Service (QoS). We show the technical and computational feasibility of computing taxes leading to a globally optimal outcome for any number of networks and application types (QoS-sensitivities), hence generalizing the results from previous works.

Traditionally cellular networks have been dimensioned for providing seamless coverage and meeting the user demand with little regard to their energy consumption. Recently, the focus has been moved to seeking ways to increase the energy efficiency by better adapting to the existing users behaviors. In this paper, we are going a step further in studying a new type of disruptive service by trying to answer the question “What are the potential energy efficiency gains if some of the users are willing to tolerate delays?”. We present an analytical model of the energy usage of LTE base stations, which provides lower bounds of the possible energy gains under a decentralized, non-cooperative setup. The model is analyzed in six different scenarios (such as micro-macro cell interaction and coverage redundancy) for varying traffic and user-tolerable delays. We show that it is possible to reduce the power consumption by up to 30%.

In the current intensively changing technological environment, wireless network operators try to manage the increase of global traffic, optimizing the use of the available resources. This involves associating each user to one of its reachable wireless networks; a decision that can be made on the user side, in which case inefficiencies stem from user selfishness. This paper aims at correcting that efficiency loss through the use of a one-dimensional incentive signal, interpreted as a price. While the so-called Pigovian taxes allow to deal with homogeneous users, we consider here two classes with different sensitivities to the Quality of Service, reflecting the dichotomy between delay-sensitive and delay-insensitive applications. We consider a geographic area covered by two wireless networks, among which users choose based on a trade-off between the quality of service and the price to pay. Using a non-atomic routing game model, we study analytically the case of constant demand levels. We show that when properly designed, the incentives elicit efficient user-network associations. Moreover, those optimal incentives can be simply computed by the wireless operator, using only some information that is easily available. Finally, the performance of the incentive scheme under dynamic demand (users opening and closing connections over time) are investigated through simulations, our incentive scheme also yielding significant improvements in that case.

Network neutrality is the topic of a vivid and very sensitive debate, in both the telecommunication and political worlds, because of its potential impact in everyday life. That debate has been raised by Internet Service Providers (ISPs), complaining that content providers (CPs) congest the network with insufficient monetary compensation, and threatening to impose side payments to CPs in order to support their infrastructure costs. While there have been many studies discussing the advantages and drawbacks of neutrality, there is no game-theoretical work dealing with the observable situation of competitive ISPs in front of a (quasi-)monopolistic CP. Though, this is a typical situation that is condemned by ISPs, and, according to them, another reason of the non-neutrality need. We develop and analyze here a model describing the relations between two competitive ISPs and a single CP, played as a three-level game corresponding to three different time scales. At the largest time scale, side payments (if any) are determined. At a smaller time scale, ISPs decide their (flat-rate) subscription fee (toward users), then the CP chooses the (flat-rate) price to charge users. Users finally select their ISP (if any) using a price-based discrete choice model, and decide whether to also subscribe to the CP service. The game is analyzed by backward induction. As a conclusion, we obtain among other things that non-neutrality may be beneficial to the CP, and not necessarily to ISPs, unless the side payments are decided by ISPs.

As the IPv4 address pool is being exhausted, it becomes urgent to find a way to migrate IPv4 network architectures to IPv6, or to reduce the use of IPv4 addresses. In this paper, we discuss a strategy known as “Address + Port” translation, which consists in several users sharing the same IPv4 address and being distinguished by a range of port numbers. Of critical importance for the feasibility of such a mechanism is the knowledge of the minimum number of ports to allocate to users so that no service degradation is perceived. To that extent, we analyse the port consumption of the most port-consuming Internet applications, Web browsing, and present some aggregate port consumption curves for the student population of our campus. Our results suggest that a port range of 1000 ports is totally transparent to users (which would allow to share a single IPv4 address among 64 users), while 400 ports (i.e., 150 users per address) is sufficient for most of users. Finally, the number of users per address could be further improved by benefiting from statistical multiplexing, i.e., using dynamical instead of fixed port range allocation.

The case for a competitive market operated by a Mobile Network Operator (MNO) and a Mobile Virtual Network Operator (MVNO) is analysed in the paper. The resource that is leased by the MNO to the MVNO is spectrum. The MNO and the MVNO compete "a la Bertrand" posting subscription prices and the mobile users may choose to subscribe to one operator. The scenario is modeled by a three-level game comprising a bargaining game, which models the spectrum leasing by the MNO; a competition game, which models the price competition between the MNO and the MVNO; and a subscription game, which models the subscription choice by the mobile users, and the outcome of which may be either not to subscribe, to subscribe to the MNO or to subscribe to the MVNO. The game is solved through backward induction, and each level has a specific solution concept: Shapley value, for the bargain; Nash equilibrium, for the competition; and Wardrop equilibrium, for the subscription. The paper assesses which conditions lead to an equilibrium where the competition does take place, which are expressed as restrictions for the spectrum leasing price agreed at the bargaining, and the spectrum efficiency improvement achieved by the MVNO. Furthermore, it argues that the amount of the leased spectrum should be fixed exogenously in order to achieve optimal user and social welfares.

The concept of trust has recently been introduced in the context of peer-to-peer networks, in order to deal with uncertainty regarding the behavior of imperfectly known agents. In this paper, we apply the notion of trust to situations of collaborative work, more precisely of document editing. We suggest to use trust to compute a satisfaction score for each participant, in order to help select the successive editors of the document so as to avoid unnecessary readings by all collaborators after each modification. We assume that the document development process ends when all collaborators are satisfied with the quality of the document. Two mechanisms using trust to improve that process are proposed, and compared to the situation without trust. Extensive simulations suggest that trust can improve the efficiency of the collaborative work, while it can be implemented in a distributed manner.

The 6LoWPAN layer was recently introduced as a means to enable the transmission of IPv6 datagrams over Low-power and Lossy Networks (LLNs). That layer provides schemes for fragmenting the network-layer datagrams and reassembling link-layer fragments. However, the loss of one fragment of the original datagram will lead to the loss of the latter, and eventually the retransmission of all the fragments, which is not energy-efficient. Simple Frame Forwarding and Recovery (SFFR), introduced by Thubert and Hui, is a simple end-to-end scheme for coping with MAC-layer losses in multi-hop LLNs. In this paper, we study the performance of SFFR in multi-hop wireless networks, in terms of energy consumption. Numerical results show that SFFR offers an efficient alternative to classical, hop-by-hop error-control schemes like ARQ (Automatic Repeat Request).

This paper presents an experimental study of TCPHC, a novel algorithm for compressing the Transmission Control Protocol (TCP) header to reduce its overhead in IPv6-enabled Low-power and Lossy Networks (6LoWPANs). Results show that TCPHC outperforms TCP both in low-loss and high-loss networks. In fact, TCPHC can reduce the TCP header to 6 bytes in more than 95% of the cases. Moreover, experimental results show that our TCP header compression algorithm reduces the energy consumption by up to 15%.

Search engines are essential actors for web browsing. We analyze here the economic competition between search engines earning money from ad word auctions. We develop a two-level game where at the largest time scale search engines decide which allocation rule to implement, between revenue-based and bid-based, and at the lowest time-scale advertisers decide how to split their advertising budget between the two search engines, depending on the benefits this will bring to them. The game at the largest time scale is solved using backward induction, the search engines anticipating the reactions of advertisers. We describe the advertisers best strategies and show how to determine, depending on parameters, an equilibrium on the ranking rule strategy for search engines, this may explain Yahoo!'s move to switch from bid-based to revenue-based ranking to follow Google's strategy.

The telecommunication world keeps evolving, with the development of new technologies, and operators have to wonder if investing in the costly infrastructures and potential licenses as well as maintaining existing technologies is worthy. This has to be investigated in a competitive context. We propose in this paper to model and analyze a three-level game between two operators. At the highest level, operators decide which technology to implement, possibilities being here 3G, WiFi and WiMAX. At the intermediate level, they fix their service price, while at the lowest level, customers choose their provider depending on price and quality of service. The model is analyzed by backward induction, where decisions at a level depend on the equilibria at the lower levels. Different real-life cost scenarios are studied, depending on whether or not operators already own the infrastructures and licenses: our model should help understanding their final decision.

Network coding (NC) is a promising technique to improve throughput in wireless mesh networks. However, some previous studies have found that the actual performance improvements offered by NC may be much lower than the gains predicted by theory. This is especially so when the bulk of the traffic carried by the mesh network is composed of TCP flows. By means of both mathematical modeling and ns-2 simulations, we explore some issues due to random packet loss in coded mesh networks. Our results illustrate how the use of network coding may induce synchronization between TCP flows; they also suggest that, under particular conditions of random packet loss, the aggregate throughput may actually be lower when NC is used.

We apply the notion of trust to situations of document editing, to select the successive editors of the document and avoid unnecessary readings by all collaborators after each modification. Two mechanisms using trust to improve that process are proposed, and compared to the situation without trust. Simulation results suggest that trust can improve the efficiency of the collaborative work.

In 3G wireless technologies, competitive operators are assigned a fixed part of the spectrum from long-term auctions. This is known to lead to utilization inefficiencies because some providers can be congested while others are lightly used. Moreover it forbids the entrance of new candidate providers. There is now a stream of work dealing with spectrum sharing among providers to lead to a better utilization. In this paper, we study an intermediate model of price competition between two providers having a fixed (licensed) part of the spectrum, but where a remaining part (an unlicensed band) can be used in case of congestion, and is therefore shared. We discuss the existence and uniqueness of the Nash equilibrium in the (static) pricing game when demand is distributed among providers according to Wardrop's principle so that users choose the least expensive perceived price (when congestion pricing is used), and investigate the influence of the shared band on social and user welfare.

In the telecommunication world, competition among providers to attract and keep customers is fierce. On the other hand customers churn between providers due to better prices, better reputation or better services. We propose in this paper to study the price war between two providers in the case where users' decisions are modeled by a Markov chain, with price-dependent transition rates. Each provider is assumed to look for a maximized revenue, which depends on the strategy of the competitor. Therefore, using the framework of non-cooperative game theory, we show how the price war can be analyzed and show the influence of various parameters.

WiMAX/IEEE 802.16 Broadband Wireless Access (BWA) is a very open technology with various possible services. While different technical aspects of that technology have been addressed, the pricing of WiMAX has received little attention until now. In this paper, we analyze the performance of a WiMAX pricing scheme that we proposed in previous works. Through extensive simulations, we evaluate the importance of a well-planned WiMAX pricing framework by comparing our proposed scheme with a basic one. Then, we study the sensitivity of the proposed pricing scheme with regard to the parameters values choices. We show that our proposed pricing scheme still provides better revenue than a basic one for a difference up to 30% more or less than the best values chosen.

WiMAX/IEEE 802.16 system is a very open Broadband Wireless Access (BWA) technology with different possible services. Pricing of WiMAX is an important topic with different optimization possibilities. In a previous paper, we investigated WiMAX pricing for real-time applications (Unsolicited Grant Service (UGS) and real-time Polling Service (rtPS) QoS classes of WiMAX). We add the Best Effort (BE) QOS class in this paper in order to have a rather complete WiMAX environment. The system model is described. Then we consider two different BE pricing mechanisms: fixed price and auction-based variable price. The proposed methods are evaluated through simulations in order to have some interesting comments and highlights for WiMAX pricing.

Migration processes of customers between alternative providers are becoming more and more relevant. Providers competing for migrating customers may adopt a delaying strategy to retain customers who are willing to leave, facing regulatory sanctions for that unfair behaviour. The contribution of this paper is to propose a game-theoretic model to describe the resulting competition among providers. For that model, both stable and unstable Nash equilibria are shown to exist and the providers' equilibrium strategies can be derived, in general numerically. In the stable equilibrium case the delaying strategy predicted by the model introduces a mean delay that is a strongly nonlinear (decaying) function of the sanction value.

Pricing telecommunication networks has become a highly regarded topic during the last decade, in order to cope with congestion by controlling demand, or to yield proper incentives for a fair sharing of resources. On the other hand, another important factor has to be brought in: there is a rise of competition between service providers in telecommunication networks such as for instance the Internet, and the impact of this competition has to be carefully analyzed. The present paper pertains to this recent stream of works. We consider a slotted resource allocation game with several providers, each of them having a fixed capacity during each time slot, and a fixed access price. Each provider serves its demand up to its capacity, demand in excess being dropped. Total user demand is therefore split among providers according to Wardrop's principle, depending on price and loss probability. Using the characterization of the resulting equilibrium, we prove, under mild conditions, the existence and uniqueness of a Nash equilibrium in the pricing game between providers. We also show that, remarkably, this equilibrium actually corresponds to the socially optimal situation obtained when both users and providers cooperate to maximize the sum of all utilities, this even if providers have the opportunity to artificially reduce their capacity.

WiMAX/IEEE 802.16 broadband wireless access (BWA) is a very open technology with different possible services that is likely to be widely used in metropolitan access networks in the near future. While the technical aspects of that technology are now quite well established, the economic implications of the introduction of WiMAX technology have received little attention. In this paper, we consider the problem of pricing WiMAX sessions: we propose some simple pricing mechanisms aimed at coping with congestion while maintaining a sufficient quality of service and/or yielding a sufficient revenue to the operator. Our mechanisms are evaluated under a specific model of user preferences and WiMAX situations, and the implications of some parameter choices are observed.

We compare two possible mechanisms to manage a peer-to-peer storage system, where participants can store data online on the disks of peers in order to increase data availability and accessibility. Due to the lack of incentives for peers to contribute to the service, we suggest that either each peer's use of the service be limited to her contribution level (symmetric schemes), or that storage space be bought from and sold to peers by a system operator that seeks to maximize profit. Using a noncooperative game model to take into account user selfishness, we study those mechanisms with respect to the social welfare performance measure, and give necessary and sufficient conditions for one scheme to socially outperform the other.

We study a buffer that implements the Random Early Detect/Discard (RED) mechanism to cope with congestion, and offers service differentiation by proposing a finite number of slopes at different prices for the RED probability. As a characteristic, the smaller the slope, the better the resulting QoS. Users are sensitive to their average throughput and to the price they pay. Since the study of the noncooperative game played is rendered difficult by the discrete nature of the strategy sets, and since it is not likely that users have a perfect knowledge of the game but only know their experienced utility, we introduce a decentralized learning algorithm to progressively reach a Nash equilibrium over time. We examine the effect of prices on the final game outcomes.

Extending the coverage of cellular networks in an ad hoc fashion appears as a promising solution for the future. This paper considers the economic aspects of such an hybrid network, especially focusing on incentives: what subscription discount should be granted to users who accept to transfer traffic with respect to users who refuse it? Does offering such an option improve the net benefit of a wireless Internet access provider?Based on models for user preferences, we establish that allowing multi-hops in cellular networks can actually be profitable to the provider, especially in densed regions, since it permits to install fewer access points to cover a region, and that economy in terms of installation and maintenance costs exceeds the eventual loss in revenue due to fee discounts.

In this paper, we design a bandwidth pricing mechanism that solves congestion problems in communication networks. The scheme is based on second-price auctions, which are known to be incentive compatible when a single indivisible item is to he sold (users have no interest to lie about the price they are willing to pay for the resource) and to lead to an efficient allocation of resources in the sense that it maximizes social welfare. We prove these properties when an infinitely divisible resource (bandwidth on a communication link) is to he shared among users who are allowed to submit several bids when they want to establish a connection. Our scheme is highly related to the progressive second price auction of Lazar and Semret where players bid sequentially until an (optimal) equilibrium is reached. While keeping their incentive compatibility and efficiency properties, our scheme presents the advantage that the multibid is submitted once only, saving a lot of signalization overhead.

Pricing has become mandatory to overcome congestion and to offer service differentiation in communication networks. Whereas many pricing schemes have been designed in the literature, we focus here on the so-called progressive second price auction that allocates bandwidth on an auction-basis: users sequentially declare the amount of bandwidth they expect and how much they value it. At each time, the network allocates bandwidth to users with the highest willingness-to-pay and charge them with the bid of those excluded from the game by their presence. Convergence, efficiency and incentive compatibility have been verified in the literature for this scheme. Nevertheless one degree of freedom still remains in the model, namely the reserve price that is the minimal unit price at which the network accepts to sell the bandwidth. We propose here to determine the reserve price maximizing the network revenue. This analysis is based on the assumption that the demand functions and distribution of the (a priori random) number of users in the network are known.

In this paper, we design a pricing mechanism for downlink transmission power in cellular networks. We propose to adapt an auction-based scheme, that we have defined in a previous work for selling an infinitely divisible resource, to the context of CDMA wireless communications. The scheme aims at solving congestion problems by acting on user demand through prices, and is particularly well-suited for situations where the number of users changes over time.We establish that our mechanism is incentive compatible, i.e. the strategy that yields the highest utility to a player is to truthfully reveal her valuation for the service. The efficiency of the power allocation in terms of social welfare is also proved.