Radio-frequency coverage maps (RF maps) are essential in wireless communication, but obtaining them through site surveys can be labor-intensive and sometimes impractical. To address this challenge, we propose RecuGAN, a generative adversarial network (GAN)-based approach for generating RF maps. RecuGAN leverages the principles of information maximizing GAN (InfoGAN) to capture latent properties of RF maps, enabling unsupervised categorization and generation of new and diverse RF maps. Unlike traditional methods, RecuGAN does not require labeled data or conditional input, reducing complexity, time, and cost. We enhance the RecuGAN objective function with a customized gradient penalty-based Wasserstein GAN (WGAN) function and a gradient-based loss function for stable training and accurate map generation. We also provide the option to incorporate multiple generators in RecuGAN, enabling high-resolution RF map generation. As demonstrated through extensive training with both experimental and simulation data, RecuGAN can synthesize diverse high-quality RF maps and categorize them based on the RSS distribution. Compared to a UNet-based conditional GAN (cGAN), RecuGAN achieves a mean average percentage error (MAPE) of 1.18%, outperforming the cGAN model, which achieves a MAPE of 2.5%.

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverage estimation. Conducting site surveys to obtain RF maps is labor-intensive and sometimes not feasible. In this paper, we propose radio-frequency adversarial deep-learning inference for automated network coverage estimation (RADIANCE), a generative adversarial network (GAN) based approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a semantic map, a high-level representation of the indoor environment to encode spatial relationships and attributes of objects within the environment and guide the RF map generation process. We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment. RADIANCE incorporates this loss function along with the antenna pattern to capture signal propagation within a given indoor configuration and generate new patterns under new configuration, antenna (beam) pattern, and center frequency. Extensive simulations are conducted to compare RADIANCE with ray-tracing simulations of RF maps. Our results show that RADIANCE achieves a mean average error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity index (MS-SSIM) of 0.80.

While Federated Learning (FL) enables training by only sharing model updates rather than data, FL can still be prone to privacy leaks. Therefore, many efforts have been made to adopt homomorphic encryption or differential privacy approaches to prevent this. However, these solutions come with several issues that may limit their widespread adoption in applications that involve sensitive data sitting in silos. Such issues include but are not limited to trust in the aggregation server, the accuracy of the model, potential collusion among clients, and limited aggregation function support. To address these issues, we advocate using secure Multiparty Computation (MPC) to offer privacy-preserving computation. Specifically, we propose an FL framework that enables outsourcing the model aggregation to MPC parties on untrusted cloud environments and offers correctness verification to the model owners. Unlike differential privacy-based solutions, the proposed framework offers the same level of accuracy as models that are trained on the clear and minimize the possibility of collusion among clients and MPC parties. We implemented and evaluated the proposed framework under various conditions. The results showed that our framework can match the accuracy of centralized FL training while maintaining the required level of privacy and security in malicious cross-silo settings.

Committee-based permissionless blockchain approaches overcome single leader consensus protocols’ scalability issues by partitioning the outstanding transaction set into shards and selecting multiple committees to process these transactions in parallel. However, by design, shard-based blockchain solutions are vulnerable to Sybil attacks. An adversary with enough computational/hash power can easily manipulate the consensus protocol by generating multiple valid node identifiers/IDs (i.e., multiple Sybil committee members).Despite the straightforward nature of these attacks, they have not been systematically investigated. This article fills this research gap by analyzing Sybil attacks in shard-based consensus of proof-of-work blockchain systems. Specifically, we provide a detailed analysis for Elastico, one of the prominent shard-based blockchain models. We show that the proof-of-work technique used for ID generation in the initial phase of such protocols is vulnerable to Sybil attacks when an adversary (could be a group of colluding nodes) possesses enough hash power. We analytically derive conditions for two different Sybil attacks and perform numerical simulations to validate our theoretical results under various parameters. Further, we utilize the BlockSim simulator to validate our mathematical computation, and results confirm the correctness of the analysis.

Algorand is a public proof-of-stake (PoS) blockchain with a throughput of 750 MB of transactions per hour, 125 times more than Bitcoin. While the throughput of Algorand depends on the participation of most of its nodes, rational nodes may behave selfishly and not cooperate with others. To encourage nodes to participate in the consensus protocol, Algorand rewards nodes in each round. However, currently Algorand does not pay transaction fees to participating nodes, rather storing it for future use. In this paper, we show that this current approach of Algorand motivates selfish block proposers to increase their profits by creating empty blocks. Such selfish behavior reduces the throughput of Algorand. Therefore, the price of Algo will decrease in the long run. Because of this price reduction, nodes will leave Algorand, compromising its security. Moreover, lack of an appropriate mechanism to pay fees to participants causes additional issues, such as lack of transparency, centralization, and inability of nodes to prioritize transactions. To overcome this challenge, we design a perfectly competitive market and propose an algorithm for computing optimal transaction fees and block size in Algorand We also propose an algorithm that reduces the cost of Algorand, without compromising its security. We further simulate the Algorand network and show how the optimal transaction fee and block size can be calculated in practice.

This study proposes a mechanism and an instructional design in order to foster well-organized peer learning based on game theory (PD_PL). The proposed mechanism uses prisoner’s dilemma (PD), the most widely known example of game theory in a dynamically blended and collaborative learning environment. PD_PL maps the strategy and payoff concepts found in PD onto a peer learning (PL) atmosphere. As is the case in PD, any greater effort made by both participants in PD_PL results in increased learning improvement. PD_PL was run in several sessions consisting of four separate university courses with 142 computer engineering students. We gave a pretest and a posttest before and after the PD_PL sessions, respectively. Then, we used paired Hotelling’s T-Square function to analyze the pretests and posttests and investigate the impacts of PD_PL and the proposed instructional design on students’ learning improvement. We had some missing data because some students were absent from some of the PD_PL sessions. As a requirement of paired Hotelling’s T-Square function, we applied the mean value, median value, k-nearest neighbor (KNN), and fuzzy k-mean clustering (FKM) methods in order to deal with the problem of imputing missing data. The results of paired Hotelling’s T-Square indicate that PD_PL is a successful mechanism for encouraging the active participation of students in the PL process. Further analysis shows that PD_PL had sometimes even enhanced learning by up to 127.69%. We also observed that the senior students used PD_PL more successfully than the first-year students. The results of our subjective evaluation showed that the majority of the respondents found PD_PL to be an attractive and efficient tool for learning enhancement. Anyone interested in instructional designing for PL environments will find this study interesting. The mechanism could also be run in a learning management system (LMS) to prepare an effective situation of collaborative learning situation.

Advances in the unmanned aerial vehicle (UAV) design and capability, as well as decreases in the manufacturing cost, have opened up applications of UAVs in various fields, including surveillance, firefighting, cellular networks, and delivery purposes. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks among UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a partially observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm’s operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake Byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. Despite its promise, one relatively understudied aspect of this protocol has been the incentive compatibility of its reward sharing approach, without which cooperation among rational network users cannot be guaranteed, resulting in protocol failure. This paper is the first attempt to address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario in Algorand, we first show that even a small number of non-participating users (due to insufficiency of the expected incentives) can result in the network failing to append new transaction blocks. We further show that this effect, which was observed in simulations, can be formalized by means of a game-theoretic model that realistically captures the strategic interactions between users in Algorand. Specifically, we formally prove that mutual cooperation under the currently proposed reward sharing approach in Algorand is not a Nash equilibrium. To remedy this, we propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.

While the Plug-in Electric Vehicles (PEVs) are gaining popularity, Vehicle-to-Grid (V2G) technology is becoming a reality. In V2G, a PEV provides energy as well as consumes it. Since the battery of a PEV can store a small amount of electric power, a large number of PEVs must be combined to offer useful services to the grid. However, these vehicles must be managed to provide controlled services according to the need of the grid leading to the introduction of an aggregator. This work assumes a system of multiple aggregators to which a PEV can choose to subscribe. An aggregator charges its subscribers for the V2G services. To maximize their profits, the aggregators vie with each other to get the market share. This competition dictates the prices and it is crucial for each competitor to choose an optimal price. The competition among aggregators is influenced by several factors. In this work, we analyze this competition by modeling the problem as a sequential game, in particular, using the Stackelberg Leadership Model. Solving the model provides the optimal prices. We also analyze the same problem by modeling it as a simultaneous game using the Cournot Competition Model and compare the game results with that of the Stackelberg game. We conduct an extensive evaluation of the game results to demonstrate the influence of different factors on optimal behavior.

With the ever-increasing problems of higher fuel costs and greater traffic congestion, shipping and long-distance travel via interstates and highways continues to become more expensive in terms of time and money. With the advent of semi-and fully-autonomous vehicles, platooning is designed to decrease the amount of fuel used and decrease the space between vehicles to help lower costs and reduce congestion. While much work has been done regarding predetermined platoons with homogeneous vehicles, less work has been done dealing with dynamic and heterogeneous platoons. Dynamic platooning with heterogeneous vehicles open a new horizon of problems with the introduction of several extra variables including dynamic platoon creation and management, untrusted users, differences in vehicle mechanics, and differences in fuel savings. One major problem facing dynamic, heterogeneous platooning is the leadership forfeiture abuse. Because there are currently no incentives for vehicles to lead in these platoons and the savings are much better when being a follower, it is more advantageous for a leader to forfeit their position and step down to become a follower and gain more benefits. In this paper, therefore, we propose a protocol, named as Leadership Incentives for Platoons (LIPs), which is suitable for dynamic platooning with heterogeneous vehicles. While the protocol provides a payment system that incentivizes individuals to lead, it is designed and implemented using the blockchain technology to offer a distributed secure environment for untrusted vehicles to interact. We demonstrate the application of the proposed protocol on a synthetic case study and evaluate the protocol by analyzing the time required for platooning operations/transactions as well as performing a usability test on a potential user group.

In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12, 19, and 21 percent improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.

In this paper, a novel protocol is proposed in which mobile terminals (MT) form a virtual Multiple-input Multiple-output (MIMO) uplink by means of device relaying on Device to Device (D2D) tier in 5G Cellular Network. The competitive scenario is considered in which each of the selfish MTs tries to transmit its own data and not relay others' data in the formed virtual MIMO. The main focus is to design an incentive for MTs to form the virtual MIMO and cooperate in relaying others data. A direct revelation on-line mechanism for the BS is designed, in order to assist forming a stable virtual MIMO. A self-punishment mechanism is also proposed in which MTs autonomously punish malicious MTs that do not cooperate in relaying. We prove that our designed direct revelation on-line mechanism and proposed self-punishment mechanism enforce all-cooperation (all-C) profile as a Nash equilibrium (NE), under uncertainty in the presence of MTs in the formed virtual MIMO. Our simulation results confirm that the proposed protocol, even in the competitive scenario, increases the bit rate and decreases power consumption at the same time. The proposed protocol can improve the energy efficiency up to 35 percent compared to a non-cooperative case, i.e., Single-Input Multiple-Output (SIMO) uplink. Moreover, if the multi-user MIMO transmission is used for the uplink medium access layer, the proposed protocol can improve the energy efficiency up to 42 percent compared to SIMO uplink with multi-user MIMO transmission. Under the proposed OCVM protocol with Shapley value fairness, the price of anarchy reaches to 0.78 in the competitive scenario. In addition, the energy efficiency improvement of our proposed protocol is almost robust to the preferences of MTs. Simulation results show that if BS employs our on-line mechanism and MTs autonomously punish malicious MTs, the malicious MTs cannot gain by defecting from relaying other MTs' data.

In this paper, a novel self-punishment based scheduling algorithm for a cooperative real- time content download application is designed. In the proposed protocol, selfish mobile devices autonomously form cooperative groups. For each formed group, the base station transmits the content to a selected mobile device designated as seed. Then, the seed shares the content with other mobile devices called sinks over device-to-device links. After analyzing the proposed protocol using a repeated game, new self-punishment mechanisms by revocation or by decreasing the bit rate, are proposed. Such self-punishment mechanisms enable the mobile devices in each cooperative group to autonomously punish selfish seeds without requiring any help from other mobile devices outside cooperative group. ‌Based on the proposed self-punishment mechanisms, a fair algorithm is designed to schedule the seeds in each cooperative group. Then, the designed scheduling algorithm is implemented using an Android application that is developed using Java in the Android Development Tool Bundle. The developed Android application does not depend on the operation system of mobile devices. Simulation results demonstrate that, on the average, the proposed protocol improves the energy efficiency of mobile devices to download real-time content of around 42 % compared to a traditional multicast scenario. Moreover, the proposed protocol does not let the energy efficiency of mobile devices degrade more than 11 %, on average, from the optimal solution even when all the mobile devises are selfish. The practical results show that the maximum difference in the run time of a real- time video over real-world smartphone screens is less than 500 milliseconds when the smartphones form a cooperative group using the developed Android application.

The Information Technology (IT) is observing a rising shift toward cloud computing due to its attractive on-demand storage and computing capabilities that allow moving the computing and storage load from the owner's side to the service provider's place and enjoying the data or computed results efficiently anywhere anytime. This growing use of clouds also introduces significant security concerns, as sensitive data and critical applications are increasingly being moved to clouds. Recent work also reveals different security threats, e.g., side-channel attacks, against cloud services. In this work, we address the need of improved solutions for the security management of cloud computing. We propose a moving target-based deceptive defense mechanism where the moving target idea is centered on frequent migrations of the virtual machines (VMs). We make the moves cost-efficient by modeling the problem as a signaling game between the adversary and the VMs and introducing deceptions. We solve the game and obtain two Nash equilibria. These results illustrate the best possible moves by the adversary and the corresponding strategy for the VMs that should reduce the adversary's chance of being successful at most.

The recent proposed solutions for demand side energy management leverage the two-way communication infrastructure provided by modern smart-meters and sharing the usage information with the other users. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. We propose a novel protocol to share required information among users providing privacy, confidentiality, and integrity. We also propose a new clustering-based, distributed multi-party computation (MPC) protocol. Through simulation experiments we demonstrate the efficiency of our proposed solution. The existing solutions typically usually thwart selfish and malicious behavior of consumers by deploying billing mechanisms based on total consumption during a few time slots. However, the billing is typically based on the total usage in each time slot in smart grids. In the second part of this paper, we formally prove that under the per-slot based charging policy, users have incentive to deviate from the proposed protocols. We also propose a protocol to identify untruthful users in these networks. Finally, considering a repeated interaction among honest and dishonest users, we derive the conditions under which the smart grid can enforce cooperation among users and prevents dishonest declaration of consumption.

In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs' requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD's energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional game is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared with a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions' size will increase (decrease).

In mobile networks, authentication is a required primitive for most security protocols. Unfortunately, an adversary can monitor pseudonyms used for authentication to track the location of mobile nodes. A frequently proposed solution to protect location privacy suggests that mobile nodes collectively change their pseudonyms in regions called mix zones. This approach is costly. Self-interested mobile nodes might, thus, decide not to cooperate and jeopardize the achievable location privacy. In this paper, we analyze non-cooperative behavior of mobile nodes by using a game-theoretic model, where each player aims at maximizing its location privacy at a minimum cost. We obtain Nash equilibria in static n-player complete information games. As in practice mobile nodes do not know their opponents' payoffs, we then consider static incomplete information games. We establish that symmetric Bayesian-Nash equilibria exist with simple threshold strategies. By means of numerical results, we predict behavior of selfish mobile nodes. We then investigate dynamic games where players decide to change their pseudonym one after the other and show how this affects strategies at equilibrium. Finally, we design protocols-PseudoGame protocols-based on the results of our analysis and simulate their performance in vehicular network scenarios.

In many envisioned mobile ad hoc networks, nodes are expected to periodically beacon to advertise their presence. In this way, they can receive messages addressed to them or participate in routing operations. Yet, these beacons leak information about the nodes and thus hamper their privacy. A classic remedy consists of each node making use of (certified) pseudonyms and changing its pseudonym in specific locations called mix zones. Of course, privacy is then higher if the pseudonyms are short-lived (i.e., nodes have a short distance-to-confusion), but pseudonyms can be costly, as they are usually obtained from an external authority. In this paper, we provide a detailed analytical evaluation of the age of pseudonyms based on differential equations. We corroborate this model by a set of simulations. This paper thus provides a detailed quantitative framework for selecting the parameters of a pseudonym-based privacy system in peer-to-peer wireless networks.

In mobile networks, authentication is a required primitive for the majority of security protocols. However, an adversary can track the location of mobile nodes by monitoring pseudonyms used for authentication. A frequently proposed solution to protect location privacy suggests that mobile nodes collectively change their pseudonyms in regions called mix zones. Because this approach is costly, self-interested mobile nodes might decide not to cooperate and could thus jeopardize the achievable location privacy. In this paper, we analyze the non-cooperative behavior of mobile nodes by using a game-theoretic model, where each player aims at maximizing its location privacy at a minimum cost. We first analyze the Nash equilibria in n-player complete information games. Because mobile nodes in a privacy-sensitive system do not know their opponents' payoffs, we then consider incomplete information games. We establish that symmetric Bayesian-Nash equilibria exist with simple threshold strategies in n-player games and derive the equilibrium strategies. By means of numerical results, we show that mobile nodes become selfish when the cost of changing pseudonyms is small, whereas they cooperate more when the cost of changing pseudonyms increases. Finally, we design a protocol - the PseudoGame protocol - based on the results of our analysis.

Abstract The design of efficient IEEE 802.11 physical (PHY) rate adaptation algorithms is a challenging research topic and usually the issues surrounding their implementations on real 802.11 devices are not disclosed. In this paper, we identify and evaluate the key parameters to design such algorithms. We then present a survey on existing PHY rate adaptation mechanisms and discuss their advantages and drawbacks. We also propose three new 802.11 PHY rate adaptation mechanisms, named adaptive auto rate fallback (AARF), closed loop adaptive rate allocation (CLARA), and adaptive multi‐rate retry (AMRR). AARF, proposed for low‐latency systems, has low complexity and obtains similar performance than RBAR in stationary and non‐fading wireless channels. CLARA is a culmination of the best attributes of the transmitter‐based ARF and RBAR control mechanisms with additional practical features such as adaptive fragmentation to improve multipath‐fading channel sensing and to provide feedback control signaling. AMRR is designed for high‐latency systems; it has been implemented and evaluated on an AR5212‐based device. Experimentation results show more than 20% performance improvement in throughput over the default algorithm implemented in the AR5212 MADWIFI driver. Copyright © 2008 John Wiley & Sons, Ltd.

A frequently proposed solution to node misbehavior in mobile ad hoc networks is to use reputation systems. But in ephemeral networks - a new breed of mobile networks where contact times between nodes are short and neighbors change frequently - reputations are hard to build. In this case, local revocation is a faster and more efficient alternative. In this paper, we define a game-theoretic model to analyze the various local revocation strategies. We establish and prove the conditions leading to subgame-perfect equilibria. We also derive the optimal parameters for voting-based schemes. Then we design a protocol based on our analysis and the practical aspects that cannot be captured in the model. With realistic simulations on ephemeral networks we compare the performance and economic costs of the different techniques.

Wireless social community networks are emerging as a new alternative to providing wireless data access in urban areas. By relying on users in the network deployment, a wireless community can rapidly deploy a high-quality data access infrastructure in an inexpensive way. But, the coverage of such a network is limited by the set of access points deployed by the users. Currently, it is not clear if this paradigm can serve as a replacement of existing centralized networks operating in licensed bands (such as cellular networks) or if it should be considered as a complimentary service only, with limited coverage. This question currently concerns many wireless network operators. In this paper, we study the dynamics of wireless social community networks by using a simple analytical model. In this model, users choose their service provider based on the subscription fee and the offered coverage. We show how the evolution of social community networks depends on their initial coverage, the subscription fee, and the user preferences for coverage. We conclude that by using an efficient static or dynamic pricing strategy, the wireless social community can obtain a high coverage. Using a game-theoretic approach, we then study a case where the mobile users can choose between the services provided by a licensed band operator and those of a social community. We show that for specific distribution of user preferences, there exists a Nash equilibrium for this non-cooperative game.

In a mobile ad-hoc network (MANET) architecture, there is no pre-existing fixed network infrastructure, and a mobile node in this network sends data packets to a destination node directly or through its neighbor nodes. This situation is of potential security concern since the neighbor nodes cannot be always trusted. In this paper, we design a group member authentication protocol used in a MANET. It aims to allow a set of nodes to legitimately participate in group communication and then distribute a secret group key to the approved nodes to establish secure communication with group members. Our protocol provides knowledge-based group member authentication, which recognizes a list of secret group keys held in a mobile node as the node's group membership. It employs zero knowledge proof and threshold cryptography. We then introduce our actual implementation and evaluate the behavior to ensure its successful deployment

We present an analytical model that accounts for the positions of stations with respect to the access point (AP) while evaluating the performance of the 802.11 MAC layer. Our work is based on the Bianchi model where the performance of the 802.11 MAC layer is computed using a discrete time Markov chain, but where all stations are implicitly assumed to be located at the same distance from the AP. In our model, given the position of one station, we compute its saturation throughput while considering the positions of other concurrent stations. Further, our model provides the total saturation throughput of the medium. We solve the model numerically and we show that the saturation throughput per station is strongly dependent not only on the station's position, but also on the positions of the other stations. Results confirm that a station achieves a higher throughput when it is closer to the AP, but bring out that there is a distance threshold above which the throughput decrease is fast and significant. When a station is far from the AP compared to the other stations, it contends for the bandwidth not used by the other stations. We believe that our model is a good tool to dimension 802.11 wireless access networks and to study their capacities and their performances.

Today, three different physical (PHY) layers for the IEEE 802.11 WLAN are available (802.11a/b/g); they all provide multi-rate capabilities. To achieve a high performance under varying conditions, these devices need to adapt their transmission rate dynamically. While this rate adaptation algorithm is a critical component of their performance, only very few algorithms such as Auto Rate Fallback (ARF) or Receiver Based Auto Rate (RBAR) have been published and the implementation challenges associated with these mechanisms have never been publicly discussed. In this paper, we first present the important characteristics of the 802.11 systems that must be taken into account when such algorithms are designed. Specifically, we emphasize the contrast between low latency and high latency systems, and we give examples of actual chipsets that fall in either of the different categories. We propose an Adaptive ARF (AARF) algorithm for low latency systems that improves upon ARF to provide both short-term and long-term adaptation. The new algorithm has very low complexity while obtaining a performance similar to RBAR, which requires incompatible changes to the 802.11 MAC and PHY protocol. Finally, we present a new rate adaptation algorithm designed for high latency systems that has been implemented and evaluated on an AR5212-based device. Experimentation results show a clear performance improvement over the algorithm previously implemented in the AR5212 driver we used.

We present a media-oriented mechanism for selecting the appropriate transmission mode in 802.11-based wireless LANs (WLANs). The main goal of this mechanism is to improve the effective throughput for transporting loss-tolerant multimedia traffic over a WLAN by taking into account both the application characteristics and the physical channel conditions. In particular, the proposed cross-layer mechanism exploits the robustness of multimedia coding by allowing packets with corrupted payloads reach the receiving application. The sending application specifies its quality of service requirements (data rate, BER tolerance, etc.), and the receiver selects the best transmission mode (transmission rate, modulation scheme, FEC scheme) while taking into account the time-varying channel conditions. We discuss the modifications needed for the control and data-packet headers to implement our approach in the framework of the IEEE 802.11 standards. We use ns2 simulations to contrast our scheme with an existing 802.11 rate selection algorithm. The results indicate that the proposed cross-layer approach achieves up to 5 Mbps increase in throughput and 20-meter increase in the coverage range. Furthermore, by disabling FEC from some of the standard transmission modes, we show that the goodput of loss-tolerant applications can be improved significantly.