As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties. In this article, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided . These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub—optimistic and conservative. Furthermore, we put forward asymmetric weighted moving average scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.

Primary user emulation (PUE) attacks can pose a significant threat to the deployment of a robust cognitive radio network implementing dynamic spectrum access, for an intelligent allocation and usage of already crowded spectrum bands. In this paper, we present a solution towards the PUE attacks. We present two generative adversarial net (GAN) based models to successfully emulate the primary users (PUs) in two ways. We propose a (i) dumb generator model without any "prior" knowledge of PU's feature space, (ii) a smart generator model with some "prior" knowledge about PU's transmission. We also propose two deep neural network based discriminator models to discriminate between the PU and the emulated primary users (EPU) from the corresponding generators. Both the generator and discriminator of each GAN model gets smarter with iterative and sequential GAN training. Through a testbed evaluation, we show that discriminators are able to catch ~50% of PUE attackers without the GAN training during the deployment phase. We also observe 100% accuracy for both the GAN models during training phase. Ultimately, after the GAN training, the discriminators achieved 98% and 99.5% accuracies, for dumb and smart generator models respectively, to distinguish "yet to be seen" PUE attacker.

A new framework for a secure and robust consensus in blockchain-based IoT networks is proposed using machine learning. Hyperledger fabric, which is a blockchain platform developed as part of the Hyperledger project, though looks very apt for IoT applications, has comparatively low tolerance for malicious activities in an untrustworthy environment. To that end, we propose AI-enabled blockchain (AIBC) with a 2-step consensus protocol that uses an outlier detection algorithm for consensus in an IoT network implemented on hyperledger fabric platform. The outlier-aware consensus protocol exploits a supervised machine learning algorithm which detects anomaly activities via a learned detector in the first step. Then, the transactions go through the inherent Practical Byzantine Fault Tolerance (PBFT) consensus protocol in the hyperledger fabric for ledger update. We measure and report the performance of our framework with respect to the various delay components. Results reveal that our implemented AIBC network (2-step consensus protocol) improves hyperledger fabric performance in terms of fault tolerance by marginally compromising the delay performance.

The recent advances of wireless technologies in RF environments coupled with large scale usage of such technologies has warranted more autonomous deployments of wireless systems. Machine learning techniques, that include recurrent structures, have shown promise in creating such autonomous deployments using the idea of Radio Frequency Machine Learning (RFML). In large scale autonomous deployments of wireless communication networks, the signals received from one component play a crucial role in the decision making process of other components. In order to efficiently implement such systems each component of the network should be uniquely identifiable. In this paper we propose a transmitter fingerprinting technique for radio device identification using recurrent structures, by exploiting the temporal property of the received radio signal. We design and implement three recurrent neural networks (RNNs) using different types of cell models: (i) long short term memory (LSTM); (ii) gated recurrent unit (GRU) and (iii) convolutional long short term memory (ConvLSTM), for this task. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as transmitters and collect over-the-air raw in-phase (I) and quadrature (Q) (I/Q) time series data from them using a DVB-T RTL-SDR receiver, in a laboratory setting. We exploit both the temporal variations as well as the inherent spatial dependencies in the collected I/Q time series data, to learn unique feature representations and use these as "fingerprints'" for identifying the transmitters. Experimental results reveal that the RNNs with LSTM, GRU, and ConvLSTM cells are able to correctly distinguish between the 8 transmitters with 92%, 95.3%, 97.2% accuracy respectively.

Understanding and analyzing the radio frequency (RF) environment have become indispensable for various autonomous wireless deployments. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize a RF environment. However, classical machine learning methods have their limitations and there are situations where such methods become ineffective. One such setting is where active adversaries are present and try to disrupt the RF environment through malicious activities like jamming or spoofing. In this paper we propose an adversarial learning technique for identifying rogue RF transmitters and classifying trusted ones by designing and implementing generative adversarial nets (GAN). The GAN exploits the in-phase (I) and quadrature imbalance (i.e., the IQ imbalance) present in all transmitters to learn the unique high dimensional features that can be used as “fingerprints” for identifying and classifying the transmitters. We implement a generative model that learns the sample space of the IQ values of the known transmitters and use the learned representation to generate fake signals that imitate the transmissions of the known transmitters. We program 8 universal software radio peripheral (USRP) software defined radios as trusted transmitters and collect over-the-air raw IQ data from them using a RTL-SDR in a laboratory setting. We also implement a discriminator model and show that the discriminator is able to discriminate between the trusted transmitters from fake ones with 99.9% accuracy. Finally, the trusted transmitters are classified using convolutional neural network (CNN) and fully connected deep neural networks (DNN). Results reveal that the CNN and DNN are able to correctly discriminate between the 8 trusted transmitters with 81.6% and 96.6% accuracies respectively.

Most of the cybersecurity research focus on either presenting a specific vulnerability %or hacking technique, or proposing a specific defense algorithm to defend against a well-defined attack scheme. Although such cybersecurity research is important, few have paid attention to the dynamic interactions between attackers and defenders, where both sides are intelligent and will dynamically change their attack or defense strategies in order to gain the upper hand over their opponents. This 'cyberwar' phenomenon exists among most cybersecurity incidents in the real world, which warrants special research and analysis. In this paper, we propose a dynamic game theoretic framework (i.e., hyper defense) to analyze the interactions between the attacker and the defender as a non-cooperative security game. The key idea is to model attackers/defenders to have multiple levels of attack/defense strategies that are different in terms of effectiveness, strategy costs, and attack gains/damages. Each player adjusts his strategy based on the strategy's cost, potential attack gain/damage, and effectiveness in anticipating of the opponent's strategy. We study the achievable Nash equilibrium for the attacker-defender security game where the players employ an efficient strategy according to the obtained equilibrium. Furthermore, we present case studies of three different types of network attacks and put forth how our hyper defense system can successfully model them. Simulation results show that the proposed game theoretical system achieves a better performance compared to two other fixed-strategy defense systems.

In this paper we study and implement real-time adaptation schemes for video encoding and channel selection that work in tandem to facilitate HD video streaming for secondary users in a dynamic spectrum access network. Out-of-band feedbacks on instantaneous pathloss of the signal between the transmitter and the receiver, the received signal strength indicator (RSSI) at the receiver and the quality of the reconstructed video are used to continuously determine the most apt encoding parameters. At the same time, the radio transmitter continuously adjusts the channel parameters (i.e., center frequency and channel bandwidth) based on the transmission activities of the primary users who have prioritized rights on these channels. We consider the physical limitations of the encoder along with the channel statistics to determine when to change the encoder parameters and when to switch to a new channel. We propose a multi-level threshold based mechanism to find the optimal number of encoding bit rates. We also propose a threshold based algorithm to find the best available channel between the transmitter-receiver pair. We validate our theoretical propositions on an indoor testbed using software defined radios (SDRs) and the GNU Radio suite. Live video was captured, encoded using open source H.264 software libraries, streamed using GStreamer and transmitted over the 915 MHz ISM bands with omnidirectional antennas. For the SDRs, we chose the universal software radio peripheral (USRP) B210s from Ettus Research and use them as the transmitter and the receiver. A third B210 was used to sense the energy levels on all the channels to detect the presence of primary transmissions. GNU Radio was used to build the signal processing pipeline, both for the transmitter and the receiver. We use PSNR and SSIM to measure the video quality and report experimental results that show that: (i) the video encoder and the USRP transmitter-receiver pair are able to adapt to the changing RF conditions, (ii) the adaptation schemes yield better video quality than non-adaptive schemes, and (iii) the USRPs can switch the channels fast enough allowing uninterrupted HD video streaming even when primary users preempt the secondary user's transmission.

As systems based on social networks grow, they get affected by huge number of fake user profiles. Particularly, social recommender systems are vulnerable to profile injection attacks where malicious profiles are injected into the rating system to affect user's opinion. The objective of attackers is to inject a large set of biased profiles that provide favorable or unfavorable recommendations for a product. In this paper, we propose a classification technique for detection of attackers. First, we define the attributes that provide the likelihood of a user having a profile of that of an attacker. Using user-item rating matrix, user-connection matrix, and similarity between users, we find if the ratings are abnormal and if there are random connections in the network. Then, we use fc-means clustering to categorize users into authentic users and attackers. To evaluate our framework, we use Epinions dataset and inject intelligent push and nuke attacks. These attacks make arbitrary connections to existing users and provide biased ratings. To evaluate the performance, we use precision and recall to show that fc-means clustering can identify the attackers with high accuracy and low false positives.

In this paper, we propose PreDA-a preferencebased truthful double auction for dynamic spectrum access (DSA) networks where multiple heterogeneous spectrum bands are sold by the primary users and bought by the secondary users. We consider channels' heterogeneity and multi-bids from buyers, and also consider buyers' preferences for the channels. We use the signal to interference and noise ratio (SINR) as a metric for the preference; channels with higher SINR are preferred and hence bids are more compared to the bids for channels that offer lower SINRs. In order to maximize spatial and temporal reuse of channels, we use the concept of virtual grouping of conflict-free buyers. Virtual groups allow us to transform multi-unit bids to single-unit bids. We propose a novel winner determination and pricing mechanism to allocate the unused spectrum bands to the most appropriate buyers. We validate PreDA through simulation experiments and show its performance in terms of the number of allocated bands, utilization, revenue, and fairness.

Selection of clusterheads using energy efficient clustering algorithms in a wireless sensor network (WSN) is very crucial as it affects the lifetime and performance of the network. As clusterheads and cluster members (i.e., non-clusterhead nodes) have a different energy consumption rates, it is necessary that all nodes resort to some rational scheme such that the connectivity and proper functioning of the network is not compromised. In this paper, we propose a Cost and Payment-based clustering Algorithm (CoPA) for achieving energy efficiency in wireless sensor networks under a game theoretical framework. The analysis is based on a non-cooperative, repeated general sum game, where each node behaves selfishly in order to maximize its lifespan (payoff). We demonstrate that the correlated equilibrium is a practical solution for clusterhead selection, which provides better performance than the Nash Equilibria. Correlated equilibrium provides a balance between the fully cooperative solution and the fully non-cooperative solution in terms of implementation overhead. CoPA produces a balanced distribution of responsibilities and energy consumption between the sensor nodes as well as maximizes the minimum payoff for every node. Results show that CoPA achieves better performance in terms of network lifetime and throughput compared to other popular clustering techniques.

In this paper, we propose an opportunistic scheduling algorithm for secondary users (SUs) in a dynamic spectrum access network. Scheduling is performed at the beginning of each super-frame where appropriate channel(s) are assigned to the SUs based on the expected throughput that each channel provides. We compute the expected throughput for each channel on specific time slots in a super-frame based on three factors: i) primary channel occupancy, ii) signal-to-interference-and-noise ratio, and iii) back-logged queue length of the SUs. We consider scheduling both on a frame-by-frame basis and also on a slot-by-slot basis. The state transitions are modeled using a discrete-time Markov chain (DTMC) process and their probabilities are calculated. The state transitions occur at the frame/slot boundaries. Multiple SUs are allocated the same channel on the same time slot as long as there is no interference between them. Such non-interfering sets of SUs is found by considering the expected throughput of each user on all candidate channels. In order to maximize spatial and temporal reuse of channels, the non-interfering sets are assigned to as many channels as possible on maximum possible time slots. Performance of the proposed scheduling schemes is validated using simulations where we measure metrics such as throughput, number of slots allocated, fairness, and blocking probability. We also show the efficacy of the queue-aware scheduling by comparing with one that does not consider the queues.

Online social networks have become an effective channel for influencing millions of users by facilitating exchange and spread of information. Despite recent works on modeling information diffusion in social networks, the complexity of social interactions makes quantification of any spreading phenomenon in social networks a challenging task. Most of the research in this area rely on empirical or statistical approaches without considering the temporal aspects and the carrying capacity of the networks. In this paper, we capture the temporal evolution of information spread in a social network using linear ordinary differential equations (ODEs). Our proposed model shows the influence of users and their temporal actions on the carrying capacity. We validate the diffusion process across the network using a dataset collected from Digg which is a popular social news sharing website. The results show that our dynamic carrying capacity PDE model is able to predict with high accuracy how the information diffuses in the network during the different phases of the lifetime of a news story. We also propose a model to represent the carrying capacity based on the portion of the influenced users.

Traditional recommender systems assume that all users are independent and identically distributed, and ignores the social interactions and connections between users. These issues hinder the recommender systems from providing more personalized recommendations to the users. In this paper, we propose a social trust model and use the probabilistic matrix factorization method to estimate users taste by incorporating user-item rating matrix. The effect of users friends tastes is modeled using a trust model which is defined based on importance (i.e., centrality) and similarity between users. Similarity is modeled using Vector Space Similarity (VSS) algorithm and centrality is quantified using two different centrality measures (degree and eigen-vector centrality). To validate the proposed method, rating estimation is performed on the Epinions dataset. Experiments show that our method provides better prediction when using trust relationship based on centrality and similarity values rather than using the binary values. The contributions of centrality and similarity in the trust values vary with different measures of centrality.

One of the major challenges in a wireless sensor network (WSN) is to extend the network's lifetime by minimizing the energy consumption. One of the ways to do so is to reduce network congestion as it increases delays and introduces additional packet collisions- thus, adversely affecting network performance. In this paper, we analyze this issue in routing and take an evolutionary game theoretic approach to show how sensor nodes in a WSN could evolve their routing strategies to transmit data packets in an efficient and stable manner. We derive the equilibrium state for the routing game and prove that there is no mutant- an individual node that adopts another strategy to invade the evolutionary stable strategy (ESS). In addition, we introduce a replicator dynamic model to show the behavior of nodes with various strategies over time. The proposed equilibrium solution aims to alleviate congestion and thereby improves the network lifetime. Simulation results show that the proposed system is successful in converging strategy choices to ESS even under dynamic network conditions.

In this paper, we present and implement a low-cost yet effective architecture that enables dynamic spectrum access (DSA) for any type of network. Our intention is to break the cost-scalability barrier and show that a complete system level solution for a database-assisted DSA system can be implemented with standard servers and inexpensive software configurable RF chips, thereby achieving economics of scale. First, we present the overall architecture that is capable of providing networks of any size to perform in-band and out of band channel access in a dynamic manner. The two main components of this architecture are the received signal strength indicator (RSSI) sensing network and the DSA server. For the RSSI sensing network, we built wired and wireless spectrum sensors that operate on 280–930 MHz using low-cost off the shelf software configurable RF (SCRF) chips. To get the RSSI values on a set of bands, we use generic micro-controllers to program the operating parameters (scan range, center frequency, bandwidth resolution, demodulation scheme and scan rate) of the SCRF chips. The wireless sensors transmit the sensed RSSI values to the nearest Ethernet-enabled hub using a light-weight communication protocol. The hub aggregates the data from multiple sensors and streams to the DSA server using UDP over IP. On receiving the real-time RSSI values from various sensors, the DSA server stores them in database engine with other meta data. Entries from the database are used by the channel allocation service that finds the best channel for the inquiring DSA nodes. To demonstrate the efficiency of the implemented database-assisted DSA system, we compare it to a legacy system and show the benefits in terms of resilience to jamming, channel relinquishment on primary arrival, and best channel determination and allocation. We also show the performance gains in terms of frame error rate (FER) and spectral efficiency. Finally, we compare the RSSI sensitivity of the low-cost sensors to that of a professional spectrum analyzer.

One of the ways to harness the radio bands that are not used by their primary owners, is to allow opportunistic use of the bands to be used by secondary users such that they do not interfere with the primary users. Most often, multiple secondary users contend to acquire the bands that are available. Proper scheduling techniques can resolve such contentions. However, all scheduling techniques have their own pros and cons. In this paper, we propose scheduling algorithms for dynamic spectrum access (DSA) networks that allocate channels by exploiting favorable instantaneous channel conditions of secondary users and the transmission activities of primary users. We propose two scheduling algorithms- the first one focuses on maximizing the throughput and the second one focuses on social welfare. Scheduling is performed at the beginning of each super-frame where appropriate slots(s) are assigned to secondary users. In order to better utilize the channels, multiple secondary users are allocated the same channel in the same time slot as long as there is no interference between them. However, finding such conflict-free independent set of secondary users is a NP-complete problem. We use the signal to interference and noise ratio (SINR) to find the independent sets for throughput maximization, while we use the allocation history for the achieving fairness. Using simulation, we show the performances of the proposed algorithms in terms of throughput, number of slots allocated, and fairness achieved among the users.

Abstract Collocated cognitive radio networks (CRNs) employ coexistence protocols to share the spectrum when it is not being used by the licensed primary users. These protocols work under the assumption that all spectrum bands provide the same level of quality of service, which is somewhat simplistic because channel conditions as well as the licensee's usage of allocated channels can vary significantly with time and space. These circumstances dictate that some channels may be considered better than others; therefore, CRNs are expected to have a preference over the choice of available channels. Because all CRNs are assumed to be rational and select the best available channels, it can lead to an imbalance in contention for disparate channels, degraded quality of service, and an overall inefficient utilization of spectrum resource. In this paper, we analyze this situation from a game theoretic perspective and model the coexistence of CRNs with heterogeneous spectrum as an evolutionary anti‐coordination spectrum‐sharing game. We derive the evolutionarily stable strategy (ESS) of the game by proving that it cannot be invaded by a greedy strategy. We also derive the replicator dynamics of the proposed evolutionary game, a mechanism with which players can learn from their payoff outcomes of strategic interactions and modify their strategies at every stage of the game and subsequently converge to ESS. Because all CRNs approach ESS based solely upon the common knowledge payoff observations, the evolutionary game can be implemented in a distributed manner. Finally, we analyze the game from the perspective of fairness using Jain's fairness index under selfish behavior from CRNs. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we argue that the traditional techniques for flooding and probabilistic flooding are not applicable to cognitive radio networks under the SINR regime. We identify the causes that i) degrade node outreach even with increasing deployment density under the SINR model and ii) lead to duplicate transmissions under the Boolean model. Further performance degradation occurs due to the additional constraints imposed by the primary users in such networks. To increase node outreach in interference-limited cognitive radio networks, we propose a modified version of probabilistic flooding that uses lower message overhead without compromising network connectivity. This is achieved by having just enough number of neighbors of a node to rebroadcast to others. The subset of neighbors that are selected to broadcast is decided on the number of neighbor a nodes has, their spatial orientation with respect to each other, and the interference they might cause. Identification of such subsets reduce duplicate retransmissions which in turn reduces interference. We use a localized clustering technique in conjunction with the concept of critical density from percolation theory such that each node decides its own rebroadcasting probability in a distributed manner. Through simulations, we compare the proposed technique with flooding and probabilistic flooding. Results validated that, the proposed technique reduces number of rebroadcasts and increases node outreach both under SINR and Boolean models.1

Reliability of a cooperative decision mechanism is critical for the proper and accurate functioning of a networked decision system. However, adversaries may choose to compromise the inputs from different sets of components that comprise the system. Often times, the monitoring mechanisms fail to accurately detect compromised inputs; hence cannot categorize all inputs into polarized decisions: compromised or not compromised. In this paper, we propose a Bayesian inference model based on multinomial evidence to quantify reliability for a cooperative decision process as a function of beliefs associated with observations from the imperfect monitoring mechanism. We propose two reliability models: an optimistic one for a normal system and a conservative one for a mission critical system. We also provide an entropy measure that reflects the certainty or uncertainty on the calculated reliability of the decision process. Through simulation, we show how the reliability and its corresponding entropy changes as the accuracy of the underlying monitoring mechanism improves1.

WSN contains several challenges mostly related to the energy consumption on account of the energy paucity of the sensor nodes. In order to prevent energy consumption due to the collision of transmission signals TDMA has been emerged as an effective way for assigning transmission schedules among the sensor nodes. In this paper we proposed a novel approach to modify the TDMA and enhance the parallelism in signal transmission and utilize unused time slot in an energy efficient collision free manner using inter and intra level coloring scheme without the consideration of the concept of a global clock. A tree based sensor network representation is used for TDMA application. Non interfering time slots are assigned to the sensor nodes of the tree by coloring method. Minimal coloring of the nodes in a tree structure ensures time efficient network design. Distributed TDMA with parallel access helps in energy efficiency. Experimental results reveal the correctness of our proposed scheme even in an obstructed WSN environment. Our proposed scheme performs better compared to the other existing works, as is manifested in the results.

Secondary spectrum data falsification (SSDF) is a common attack in cognitive radio networks, where dishonest nodes share spurious local sensing data. This behavior misleads the collective inference on spectrum occupancy. The situation is more aggravated when a collaborative SSDF attack is launched by a coalition of selfish nodes. Defense against such collaborative attacks is difficult with popularly used voting based inference models. This paper proposes a method based on Bayesian inference that indicates how much the collective decision on a channel's occupancy can be trusted. Using an anomaly monitoring technique, we check if the reports sent by a node match with the expected occupancy and classify the outcomes into three categories: i) if there is a match, ii) if there is a mismatch, and iii) if it cannot be decided. Based on the measured observations over time, we estimate the parameters of the hypothesis of match and mismatch events using a multinomial Bayesian based inference. We quantitatively define the trust as the difference between the posterior beliefs associated with matches and that of mismatches. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data. We conduct simulation experiments that show that the proposed trust model is able to distinguish the attacked channels from the non-attacked ones. Also, a node is able to rank the channels based on how trustworthy the inference on a channel is. We are also able to show that attacked channels have significantly lower trust values than channels that are not.

In this paper, we propose a spectrum map aided routing protocol for a distributed cognitive radio network. We assume the presence of dedicated sensors that capture the spatio-temporal spectrum usage statistics to create the radio environment map. We exploit the map to find not only the best hops along a route but also the best available channel in terms of the expected performance. Through the use of edge nodes, which lie in the intersection of more than one sensors' domain, inter-domain routing is facilitated. The selection of each hop, the channel to be used per hop, and the transmitting power to be used considers i) protection of primary receivers, and ii) maximization of desired performance metric. In this context, we propose a novel power control mechanism that computes just enough power to maintain the desired signal-to-noise ratio for secondary communication but at the same time protects the primary receivers in the vicinity. We analyze and compute the probability of network connectivity by finding the minimum spanning tree of the graph formed by the over-lapping domains. Through simulations, we show how the proposed routing scheme works in terms of route capacity, connectivity of the network, reachability among the nodes, and number of primary receivers protected.

In this paper, we propose an auction-based routing algorithm in a distributed cognitive radio network. The routing of packets is viewed as a market-based phenomenon where channels are traded between transmitters and receivers. Message routes are determined through a series of auctions between message senders and forwarders. The use of auction facilitates the determination of the cost of bandwidth in a market-like scenario, hence the price to be paid by the senders, for each of the possible routes. The process involves the forwarders (or, bidders) bidding the price and the sender choosing the winner with the minimum bid. While the proposed schema explores all routes from the source to the destination through a repeated bidding process, the route with the minimum payment is chosen as the optimal solution that satisfies the requirement of the sender. Bidders determine their bid based on three utility values: i) signal to interference and noise ratio on a specific channel, ii) achievable capacity on each channel, and iii) the required bandwidth to satisfy the buyer's bit rate requirement. We consider two cases: i) sellers can bid for only one channel; in this case the bid is for the channel which satisfies the bit rate requirement and has the minimum price and ii) sellers can bid for a combination of non-substitutable channels that collectively satisfy the bit rate requirement. Since exploring all possible combinations of channels is NR-hard, we propose a greedy heuristic that takes O(nlogn). We validate the proposed auction based routing through extensive simulation experiments. Results are obtained for different topologies and for various number of channels and networking parameters.

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes that i) cannot decode transmissions except from their nearest neighbors, ii) are always interfered, and iii) belong to isolated components. We find the thinning probability in terms of primary and secondary densities, communication radii, and interference cancellation coefficient. Further, we show how the effective coverage radius shrinks which also adds to the thinning. Theoretical findings are validated through simulations.

Design of an efficient medium access control protocol is critical for proper functioning of a distributed cognitive radio network and better utilization of the channels not being used by primary users. In this paper, we design a contention based distributed medium access control (MAC) protocol for the secondary users' channel access. The proposed MAC protocol allows collision-free access to the available data channels and eventually their utilization by secondary users, with spectrum sensing part being handled by exclusive sensing nodes. We further introduce the provision of reservation of free channels by secondaries for extended periods to increase utilization without causing harmful interference to primaries. We demonstrate how such extended access to resources can be tuned to provide differential quality of service to the secondary users. The effectiveness of the protocol is evaluated by performing analysis and simulation. We use blocking probability, secondary usage of a secondary user and performance degradation caused to primary incumbents as performance metrics. We obtain the conditions for such extended access and try to gauge the resulting increase in utilization. Under optimal conditions, the proposed scheme enables the secondary network to utilize all available channels. The proposed scheme is shown to outperform the most sophisticated existing MAC schemes for distributed secondary networks.

This paper presents large-scale Internet measurements to understand and improve the effects of Internet path selection on perceived video quality, or quality of experience (QoE). We systematically study a large number of Internet paths between popular video destinations and clients to create an empirical understanding of location, persistence, and recurrence of failures. These failures are mapped to perceived video quality by reconstructing video clips and conducting surveys. We then investigate ways to recover from QoE degradation by choosing one-hop detour paths that preserve application-specific policies. We seek simple, scalable path selection strategies without the need for background path monitoring. Using five different measurement overlays spread across the globe, we show that a source can recover from over 75% of the degradations by attempting to restore QoE with any k randomly chosen nodes in an overlay, where k is bounded by O(ln(N)). We argue that our results are robust across datasets. Finally, we design and implement a prototype packet forwarding module called source initiated frame restoration (SIFR). We deployed SIFR on PlanetLab nodes and compared the performance of SIFR to the default Internet routing. We show that SIFR outperforms IP-path selection by providing higher on-screen perceptual quality.

Design of an efficient medium access control protocol is critical for proper functioning of a distributed cognitive radio network and better utilization of the available channels not being used by primary users. In this paper, we design a contention based distributed medium access control (MAC) protocol for the secondary users' channel access. The proposed MAC protocol allows collision-free access of the available channels and eventual utilization by secondary users, with spectrum sensing part being handled by exclusive sensing nodes. The effectiveness of the proposed MAC protocol is evaluated analytically and also through empirical simulations. We show how the protocol performs with respect to blocking probability, channel grabbing, and channel utilization1.

In this paper, we propose a pricing-based routing algorithm in a distributed cognitive radio network. The routing of packets is viewed as a market-based phenomenon where bandwidth is traded between transmitters and receivers. We propose a pricing model using which a seller determines the selling price. We also find the interference that a seller is exposed to since it determines the achievable bit-rate for the bandwidth being sold. As for the buyer, we calculate the bandwidth required from different sellers considering the respective signal to noise ratio. The seller with the minimum price is selected as the next hop relay. Thus, the total price to be paid by a source node is the sum of the prices paid at each hop in the route. Through simulation experiments, we validate the proposed scheme and show the prices for various routes1.

In cognitive radio networks, the radios continuously scan the radio spectrum and create a spectrum usage report. Due to channel uncertainty, there are inaccuracies in these reports. Oftentimes, the radios share and fuse the observed data in order to increase the accuracy of the spectrum usage. However, malicious nodes tend to send false information (i.e., attack) in order to mislead the construction of the spectrum usage report. In this paper, we use a trust model to evaluate the trustworthiness of every node and use the trust values to effectively fuse the information from all nodes. A node compares the information sent by a neighboring node with the predicted information. Based on the ratio of matches (or mismatches), the neighboring node is assigned a trust value. Then, we propose a log-weighted metric utilizing trust values to distinguish malicious nodes from others. Subsequently, we propose threshold based Selective Inversion (SI) fusion and Complete Inversion (CI) fusion to effectively combine not only the information sent by honest nodes but also utilize misleading information sent by malicious nodes. We also propose a combination of the two inversion schemes. We compare the performance of the inversion based fusion schemes with blind and trust-based fusions. Results reveal better performance for inversion based fusion schemes for various intensities of attack. We also conduct simulations to evaluate the optimal thresholds that are used for invoking the inversion based fusion schemes.

In this paper, we address the problem of incentive based routing in Dynamic Spectrum Access (DSA) networks, where each secondary node incurs a cost for routing traffic from a flow and also has a certain capacity that the node can provide to the flow. We propose a path auction scheme in which each secondary node announces its cost and capacity to the routing mechanism, both of which are considered as private information known only to the node. We design a route selection mechanism and a pricing function that can induce nodes to reveal their cost and capacity honestly (making our auction truthful), while minimizing the expected payment that needs to be given to the nodes that relay traffic (making our auction optimal). By considering capacity constraint of the nodes, we explicitly support multiple path routing. For deploying our path auction based routing mechanism in DSA networks, we provide polynomial time algorithms.

This work introduces the concept of critical sections for online first person shooter games (FPS). A critical section is a section of game-play which demands higher precision or tighter deadlines. Critical section traffic is more sensitive to network degradations than sections immediately preceding or following it. Critical sections provide game developers and network programmers a notion of relative priority of game traffic, and can identify segments whose preservation can lead to superior user perceived quality of playing FPS games on a network. By analyzing video-recordings of over 5 hours of FPS gameplay by 10 volunteers, we identify sections of FPS game-play whose degradation would cause inconsistent game-state updates resulting in user frustration. We observe that critical sections exhibit a pattern of occurrence and can account for upto 17% of game-play time. We next quantify the expected network induced degradations on critical sections for online FPS games on the Internet. Using traces from a deployment of FPS workloads on 50+ nodes in the Internet, we study network dynamics and their ensuing effect on critical sections. Using traces from this experiment, we derive the lower bound on potentially degraded game-play session on todays Internet. We argue that critical sections of FPS games can be preserved. This can allow a variety of network architectures to better deliver higher perceptual experience when deployed on the Internet. Overall, our results have implications for FPS game-design, network provisioning, and game quality evaluation.

In this paper, we address the problem of Optimal Content Delivery (OCD) in Dynamic Spectrum Access (DSA) networks, where the source of a flow sends data traffic to the destination in exchange for some monetary benefit, such as a subscription fee. Also, each intermediate secondary node incurs a cost for routing traffic. We propose a path auction based content delivery mechanism in which each secondary node announces its cost (considered as private information) to the auction mechanism. Based on the announced costs, the optimal flow rate between the end nodes is determined, a multi-path route is chosen and payments are made to the nodes that route traffic, such that the profit of the source node is maximized in sending data to the destination. Furthermore, the auction mechanism is strategy-proof, i.e., it can induce the intermediate nodes to truthfully declare their costs. We provide polynomial time algorithms for implementing our auction based content delivery mechanism in DSA networks.

In this paper, we address the problem of incentive based routing in Dynamic Spectrum Access (DSA) networks, where each secondary node incurs a cost for routing traffic from a flow and also has a certain capacity that it can provide to the flow. We propose a path auction based routing mechanism in which each secondary node announces its privately known cost and capacity, based on which a route is chosen and payments are made to the nodes. We design the route selection mechanism and the pricing function in a manner that can induce nodes to reveal their cost and capacity honestly, while minimizing the payment that needs to be given to the nodes that relay traffic, thereby making our path auction optimal. In our proposed mechanism, the optimal route over which traffic should be routed and the payment that each node should receive can be computed in polynomial time. Simulation results suggest that our mechanism can ensure truthful reporting of both cost and capacity while making a payment less than that required for VCG based least cost path routing.

In this paper, we address the problem of dynamic channel access by a set of cognitive radio enabled nodes (e.g., broadcast access points), where each node acting in a selfish manner tries to access and use as many channels as possible, subject to the interference constraints. We model the dynamic channel access problem as a modified Rubinstein-Ståhl bargaining game. In our model, each node (player) negotiates with the other nodes to obtain an agreeable sharing rule of the available channels, such that, no two interfering nodes use the same channel. We solve the bargaining game by finding Subgame Perfect Nash Equilibrium (SPNE) strategies of the game. First, we consider finite horizon version of the bargaining game and investigate its SPNE strategies. We then extend these results to the infinite horizon bargaining game. Furthermore, we identify Pareto optimal equilibria of the game for improving spectrum utilization. The bargaining solution ensures that no node is starved of channels. We also conduct extensive simulations to study how the “self-gain” maximizing strategy of the players impact system wide performance.

IEEE 802.22 networks consist of base stations and consumer premise equipments (CPEs) where the base station in each cell opportunistically accesses and allocates (uplink and downlink) channels to all the CPEs in its cell. Information on white space (unused primary channels) availability is reported by the CPEs to the base stations. Thus, a base station's effectiveness to allocate channels are based on its ability to gauge the spectrum usage at various locations. In this paper, we propose a channel usage estimation framework where a base station uses the spectrum reports from other neighboring base stations to estimate the spectrum usage scenario at any arbitrary location within its cell. Our estimation framework is based on Shepard's interpolation technique for irregular points. We propose a channel allocation scheme that minimizes interference among CPEs and maximizes white space utilization. Through simulation experiments, we demonstrate the accuracy of the estimation technique, utilization of the available spectrum, and efficiency of allocation scheme. We also show that our scheme achieves very low false positives and no false negatives. Finally, we show that the optimal number of base stations that need to be consulted is in accordance with Shepard's bounds1.

With the proliferation of multimedia content on the Internet, there is an increasing demand for video streams with high perceptual quality. The capability of present-day Internet links in delivering high-perceptual-quality streaming services, however, is not completely understood. Link-level degradations caused by intradomain routing policies and inter-ISP peering policies are hard to obtain, as Internet service providers often consider such information proprietary. Understanding link-level degradations will enable us in designing future protocols, policies, and architectures to meet the rising multimedia demands. This paper presents a trace-driven study to understand quality-of-experience (QoE) capabilities of present-day Internet links using 51 diverse ISPs with a major presence in the US, Europe, and Asia-Pacific. We study their links from 38 vantage points in the Internet using both passive tracing and active probing for six days. We provide the first measurements of link-level degradations and case studies of intra-ISP and inter-ISP peering links from a multimedia standpoint. Our study offers surprising insights into intradomain traffic engineering, peering link loading, BGP, and the inefficiencies of using autonomous system (AS)-path lengths as a routing metric. Though our results indicate that Internet routing policies are not optimized for delivering high-perceptual-quality streaming services, we argue that alternative strategies such as overlay networks can help meet QoE demands over the Internet. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

This paper presents large scale Internet measurements to understand and improve the effects of Internet path selection on perceived video quality. We systematically study a large number of Internet paths between popular video destinations and clients to create an empirical understanding of location, persistence and recurrence of failures. We map these failures to perceptual quality by reconstructing video clips obtained from the trace to quantify both the perceptual degradations from these failures as well as the fraction of such failures that can be recovered.

The main challenge in resource allocation in cognitive radio based IEEE 802.22 networks is the absence of predefined control channels. Moreover, the fleeting nature of the available spectrum also hinders the communication as the radios must relinquish the acquired channels once the primary users of those channels return. In this paper, we propose a performance metrics based data channel allocation scheme for IEEE 802.22 networks where the base station allocates interference free channels to the consumer premise equipments using a spectrum map. The base station creates the spectrum map by using the raw spectrum usage data that are shared by a small subset of consumer premise equipments. The usage data are fused at the base station using a modified version of Shepard's interpolation technique. We construct a continuous and differentiable spatial distribution of spectrum usage that the base station consults to estimate the spectrum occupancy vector at any arbitrary location in its cell. Such spectrum usage is then utilized to proactively evaluate some key network and radio performance metrics which in turn help allocating the best candidate channel to a given consumer premise equipment ensuring highest achievable performance.

The open philosophy of cognitive radio networks makes them vulnerable to various types of attacks which compromises the efficiency of these networks. One such attack is the Spectrum Sensing Data Falsification (SSDF) attack where malicious nodes report false spectrum occupancy data to others which when used leads to inference that is far from the true spectrum occupancy. Thus, there is a need to identify the malicious nodes or at least find the trustworthiness of nodes such that the data sent by malicious nodes could be filtered out. This paper proposes a scheme for trust based fusion by monitoring anomalies in advertised spectrum usage reports by secondary nodes. Such monitoring leads to evaluation of trust of a node by its neighbors. The calculated trust is then used to determine if a neighbor node's advertised data could be used for fusion or not. We provide a heuristic trust threshold for nodes to disregard malicious nodes while fusing the data, which holds good for any probability of attack. To validate our model we conduct extensive simulation experiments. Our results show that majority of the nodes are able to fuse data with greater accuracy for various probabilities or intensities of attack. We also compare our results with blind fusion scheme and observe improvement in accuracy of fusion from individual nodes' as well as overall network's perspective. We also report a very counter-intuitive observation: at lower probabilities of attack, a malicious node's contribution to the overall gain in cooperation is more than the damage done.

Byzantine attacks have been identified as one of the key vulnerabilities in cognitive radio networks, where malicious nodes advertise false spectrum occupancy data in a cooperative environment. In such cases, the resultant fused data is very different from the actual scenario. Thus, there is a need to identify the malicious nodes or at least find the trustworthiness of nodes such that the data sent by malicious nodes could be filtered out. The process is complicated by presence of noise in the channel which makes it harder to distinguish anomalies caused by malicious activity and those caused due to unreliable noisy channels.

It is our great pleasure to welcome you to the Twelfth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2011. Over the years, WoWMoM has emerged to be a flagship forum that brings together researchers from academia, industry, and government laboratories who are involved in various aspects of mobile multimedia networking technologies, ranging from communications platforms to services and applications.

Cognitive Radio Network (CRN) is one of the prominent communication technologies that is touted to drive the next generations of digital communications. In this paper, we address the vulnerabilities in such networks and analyze a common form of the Denial-of-Service attack, i.e., collaborative jamming. In particular, we model and analyze the channel availability when different jamming and defending schemes are employed by the attackers and legitimate users. Cooperative defense strategy is proposed to exploit the temporal and spatial diversity for the legitimate secondary users. Illustrative results show how to improve the resiliency in CRN against jamming attacks.

In a distributed wireless system, multiple network nodes behave cooperatively towards a common goal. An important challenge in such a scenario is to attain mutual cooperation. This paper provides a non-cooperative game theoretic solution to enforce cooperation in wireless networks in the presence of channel noise. We focus on one-hop information exchange and model the packet forwarding process as a hidden action game with imperfect private monitoring. We propose a state machine based strategy to reach Nash Equilibrium. The equilibrium is proved to be a sequential one with carefully designed system parameters. Furthermore, we extend our discussion to a general wireless network scenario by considering how cooperation can prevail over collusion using evolutionary game theory. The simulation results are provided to back our analysis. In particular, network throughput performance is measured with respect to parameters like channel loss probability, route hop count, and mobility. Results suggest that the performance due to our proposed strategy is in close agreement with that of unconditionally cooperative nodes. Simulation results also reveal how the convergence of cooperation enforcement is affected by initial population share and channel unreliability.

Cognitive radio based IEEE 802.22 wireless regional area networks have been proposed to harness the highly underutilized sub 900 MHz TV bands. In such networks, both base stations and the consumer premise equipments (CPEs) continuously perform spectrum sensing and transmit only on those channels which are not being used by the primary incumbents. In this paper, we propose a heuristic for an efficient channel allocation by a base station to the CPEs in that cell. Due to the lack of dedicated control channels, the BS and CPEs within a cell go through a process of exchanging control messages on free channels. The benefit of such sharing of their mutual spectrum usage helps the base station make informed decisions on the allocation of uplink and downlink channels to CPEs. This, in turn, guarantees no interference to and from the primary licensed incumbents. For validation of the proposed allocation scheme, we conducted simulation experiments. The results show how the proposed scheme ensures allocation to almost all the CPEs in a cell and how the nature of allocation is dependent on the total number of channels scanned, probability of getting a free channel and number of CPEs in the cell.

In this paper, we model the problem of dynamic spectrum access by a set of cognitive radio enabled nodes as an infinite horizon bargaining game. Each node (or, player) negotiates with its interfering nodes to obtain an agreeable sharing rule of the available channels. We investigate the subgame perfect equilibrium strategies of the bargaining game, abiding by which, each node can maximize its throughput against all its interfering nodes (opponents). We further study the issue of fairness in this model, in terms of the relative number of channels allocated to the nodes. Moreover, each node bargains with its interfering neighbors independently and in a distributed manner, making the system scalable.

Recently, there has been increasing demand for video streams with high perceptual quality. The capability of present day Internet links in delivering high perceptual quality streaming services, however, is not completely understood. This paper presents a trace driven study to understand QoE capabilities of present day Internet links using 51 diverse ISPs with a major presence in US, Europe and Asia-Pacific. We study a rich collection of their intra-domain links and inter-domain peering links from 38 vantage points in the Internet using both passive tracing and active probing. We provide the first measurements of link level degradations from a multimedia standpoint. While we observe that ISP are internally well connected and their present peering policies are highly conducive to streaming services, the overall perceptual quality delivered is below "acceptable". Our results indicate that the fault lies with poor intra-domain traffic engineering, BGP, and the usage of AS-path lengths as the routing metric. We also provide a comprehensive case study for every scenario we investigate. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

Identification and isolation of attackers in a distributed system is a challenging problem. This problem is even more aggravated in a wireless network because the unreliable channel makes the actions of the users (nodes) hidden from each other. Therefore, legitimate users can only construct a belief about a potential attacker through monitoring and observation. In this paper, we use game theory to study the interactions between regular and attacker nodes in a wireless network. We model the attacker node detection process as a Bayesian game with imperfect information and show that a mixed strategy perfect Bayesian Nash Equilibrium is attainable. Further, we show how an attacker node can construct a nested belief system to predict the belief held by a regular node. By employing the nested belief system, a Markov Perfect Bayes-Nash Equilibrium is reached and the equilibrium postpones the detection of the attacker node. Simulation results and their discussions are provided to illustrate the properties of the derived equilibria.

No abstract available.

In infrastructure-less sensor networks, efficient usage of energy is very critical because of the limited energy available to the sensor nodes. Among various phenomena that consume energy, radio communication is by far the most demanding one. One of the effective ways to limit unnecessary energy loss is to control the power at which the nodes transmit signals. In this paper, we apply game theory to solve the power control problem in a CDMA-based distributed sensor network. We formulate a noncooperative game under incomplete information and study the existence of Nash equilibrium. With the help of this equilibrium, we devise a distributed algorithm for optimal power control and prove that the system is power stable only if the nodes comply with certain transmit power thresholds. We show that even in a noncooperative scenario, it is in the best interest of the nodes to comply with these thresholds. The power level at which a node should transmit, to maximize its utility, is evaluated. Moreover, we compare the utilities when the nodes are allowed to transmit with discrete and continuous power levels; the performance with discrete levels is upper bounded by the continuous case. We define a distortion metric that gives a quantitative measure of the goodness of having finite power levels and also find those levels that minimize the distortion. Numerical results demonstrate that the proposed algorithm achieves the best possible payoff/utility for the sensor nodes even by consuming less power.

To prevent subscriber churn, network service providers of VoD, SDV and IPTV have a pressing need to pro-actively detect, isolate and fix outages within an access network. Network induced degradations prove to be detrimental for streaming applications. This typically leads to a poor quality of experience (QoE) for subscribers. By monitoring key functional points of the access network for traces of degradation, service providers can devise mechanisms to mitigate the problem. In this work we propose a hierarchy of exporters, collectors and ANCON (ANalysis and CONtrol) nodes that can semi-autonomously monitor, detect and isolate impairments within an access network. Exporters on the data plane gather and disseminate statistics for individual subnets, which are streamed onto "collector" nodes on an orthogonal plane. Collector nodes aggregate traffic from various exporters, and stream them onto the root of the tree (ANCON). With an even placement of exporters, root cause analysis can now take the granularity of loss rates or delay rates in individual segments or subnets of an access network. As an extension to our architecture, we show that the overlay can support instrumentations of quality evaluation for streaming video. As an example, we use a simple MOS model that is in part an extension of the ITU-T Erlang model to predict the quality of a video stream much before it reaches the end user. Extensive simulations are presented to justify the design choices made in the process.

In this paper, we use game theory to mitigate self-interference among cognitive radio based IEEE 802.22 networks such that these networks can efficiently co-exist. When a network experiences interference, it can adopt either one of two choices-switch to a new band hoping to find a non-interfering one, or stay with its current band hoping that the interfering network(s) will move away to a new band. We model the spectrum band switching process as an infinite horizon repeated game where the aim of each network (player) is to find a channel void of interference from it's neighboring networks incurring minimal cost. We investigate both pure and mixed strategy solution space of the game and show that pure strategy solution of the game is infeasible. Thus a mixed strategy is proposed which achieves subgame-perfect Nash Equilibrium. Simulation results reveal that using the proposed strategies, each network can find a clear channel quickly and at the same time incur a low cost.

We present a scalable, lightweight, no-reference framework to infer video QoE. Our framework revolves around a one time offline construction of a k-dimensional space, which we call the QoE space. The k-dimensions accommodate k parameters (network-dependent/independent) that potentially affect video quality. The k-dimensional space is partitioned to N representative zones, each with a QoE index. Instantaneous parameter values are matched with the indices to infer QoE. To validate our framework, we construct a 3-dimensional QoE space with bit-rate, loss, and delay as the principal components. We create 18 video samples with unique combinations of the 3 parameters. 77 human subjects rated these video samples on a scale of 1 to 5 to create the QoE space. In a second set of survey, our predicted MOS was compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions. An implementation of our framework on standard Linux PC shows we can compute 20 MOS calculations per second with 3 parameters and 18 partitions of the QoE space.

An underlying assumption of multi-hop data communication in ad hoc networks is that the nodes forward each others' packets. An important challenge in such scenario is to attain mutual cooperation. This paper provides a game theoretic solution to enforce cooperation in ad hoc network in the presence of channel noise. We focus on the packet forwarding process and model it as a hidden action game with imperfect private monitoring. We propose a state machine based strategy to reach Nash Equilibrium. The equilibrium is proved to be a sequential equilibrium with carefully designed system parameters. Furthermore, we extend our discussion to a general ad hoc network scenario by refining the strategy profiles to handle multi-hop packet forwarding. The simulation results illustrate the efficiency of the proposed forwarding strategies. In addition, network throughput performance is measured with respect to parameters like channel loss probability, route hop count and mobility. Results suggest that the performance due to our proposed strategy is in close agreement with that of unconditionally cooperative nodes.

In this paper, we investigate how cognitive radio (CR) enabled devices can self-organize to form a tactical mesh network and operate on non-dedicated (secondary) spectrum. Each node in the network constantly senses the environment and maintains an up-to-date spectrum usage report. This report is used by a central controller (CC) to initialize the network formation. Then the other CR nodes gradually join the mesh network in a repeated, distributed manner. We provide the detailed steps for the mesh creation and also propose some refinements. We also compute the spectral efficiency that is achieved through our algorithm. Through simulation experiments, we study the effectiveness of the proposed schemes on mesh initialization latency, control signaling, collision rate during network initialization, and spectrum utilization.

Numerous routing protocols have been proposed for wireless sensor networks, each of which is highly optimized for a certain class of traffic, like real time, reliable sense and disseminate, network reprogramming, energy efficiency and so on. However, a typical deployment demands an arbitrary communication pattern that generates multiple traffic types simultaneously. Arguably, no single routing protocol can completely cater to a deployment's various flavors. In this paper, we propose a dynamic routing framework that can replace the traditional routing layer with a collection of routing decisions. We allow application packets to carry a two-bit preamble that uniquely describes the nature of communication sought for. The framework dynamically wires the appropriate routing component from a set of well-defined suite. We conduct extensive simulation experiments that generates a concurrent mix of different traffic types - each having its own, and often conflicting, communication demands. For such an application, we show that we could meet each traffic types demands for reliability, delay, path distribution, link losses and congestion losses. We also show that service differentiation can indeed be met successfully, and practical deployments can be an imminent reality.

In this paper, we use game theory to mitigate selfinterference among cognitive radio based IEEE 802.22 networks such that these networks can efficiently co-exist. When a network experiences interference, it can adopt either one of two choices- switch to a new band hoping to find a non-interfering one, or stay with its current band hoping that the interfering network(s) will move away to a new band. We model the spectrum band switching process as an infinite horizon repeated game where the aim of each network (player) is to find a channel void of interference from it's neighboring networks incurring minimal cost. We investigate both pure and mixed strategy solution space of the game and show that pure strategy solution of the game is infeasible. Thus a mixed strategy is proposed which achieves subgame-perfect Nash equilibrium. Until now we have investigated the case where each network is within the interference range of all other networks. We seek to generalize it to the case where networks can have an arbitrary number of potential interferers. Simulation results reveal that using the proposed strategies, each network can find a clear channel quickly and at the same time incur a low cost.

This paper proposes a new clustering algorithm for global positioning system (GPS)-based mobile ad hoc networks that takes into consideration the direction of the overall traffic flow in the network. The proposed cluster leader logic algorithm is motivated by the GPS quorum hybrid routing algorithm, where clusterheads are positioned on the terrain upon a conceptual cellular grid. The proposed distributed clustering algorithm chooses the clusterheads based on the traffic flow patterns, i.e. the nodes best suited to forward and route network traffic are selected. Our approach allows a clusterhead which is overwhelmed with sending packets to identify an additional clusterhead from nodes available in its cell to share its load. Likewise, clusterheads with low load will try to relinquish their role as a clusterhead. We propose a new concept called cell fanning that allows collocated clusterheads in one cell to share routing responsibilities by declaring which cells they are responsible for routing packets to. We conducted extensive simulation experiments with enforced directional traffic patterns. Two important results are presented: power consumption per clusterhead and average queuing delay for each clusterhead. Results in terms of message overheads, number of clusterheads, power consumption, and queuing delay reveal that system performance is enhanced when clusterheads are chosen considering the direction of the traffic flow.

The cognitive radio based IEEE 802.22 wireless regional area network (WRAN) is designed to operate in the under-utilized TV bands by detecting and avoiding primary TV transmission bands in a timely manner. Such networks, deployed by competing wireless service providers, would have to self-coexist by accessing different parts of the available spectrum in a distributed manner. Obviously, the goal of every network is to acquire a clear spectrum chunk free of interference from other IEEE 802.22 networks so as to satisfy the QoS of the services delivered to the end-users. In this paper, we study the distributed WRAN self-coexistence problem from a minority game theoretic perspective. We model the spectrum band switching game where the networks try to minimize their cost in finding a clear band. We propose a mixed strategy that the competing networks must adhere to in order to achieve the Nash equilibrium. Simulation experiments have also been conducted and results corroborate with the theoretical analysis.

An inherent assumption for packet forwarding in ad hoc networks is that the nodes will cooperate i.e., nodes can rely in each other. Thus, it is extremely important that cooperation is induced and achieved in the network. In this paper, we use game theory to analyze the necessary and sufficient conditions to enforce cooperation enforced, especially when a node cannot perfectly monitor other nodespsila behaviors. We analyze a credit exchange method under a general unreliable channel and show that the packet forwarding probability can be adjusted through proper design of incentives, which in turn can be used to attain the desired Nash Equilibrium. We extend our discussion to repeated games and take several well-known strategy profiles and derive the conditions under which the cooperation can lead to a subgame perfect Nash equilibrium. In particular, we show how the unreliable channel can affect the conditions and how a reputation based strategy leads to subgame perfection even under imperfect monitoring. We further investigate collusion resistance and cooperation coalition formation using evolutionary game theory. Mathematical proofs show the existence of an upper bound on the population share of the non-cooperative nodes for an evolutionarily non-stable strategy that enforces full cooperation. This bound is shown to depend on the nodespsila belief on the continuity of the game.

Real-time services such as VoIP are becoming popular and are major revenue earners for network service providers. These services are no longer confined to the wired domain and are being extended over wireless networks. Although some of the existing wireless technologies can support some low-bandwidth applications, the bandwidth demands of many multimedia applications exceed the capacity of these technologies. The IEEE 802.16-based WiMax promises to be one of the wireless access technologies capable of supporting very high bandwidth applications. In this paper, we exploit the rich set of flexible features offered at the medium access control (MAC) layer of WiMax for the construction and transmission of MAC protocol data units (MPDUs) for supporting multiple VoIP streams. We study the quality of VoIP calls, usually given by R-score, with respect to the delay and loss of packets. We observe that loss is more sensitive than delay; hence, we compromise the delay performance within acceptable limits in order to achieve a lower packet loss rate. Through a combination of techniques like forward error correction, automatic repeat request, MPDU aggregation, and minislot allocation, we strike a balance between the desired delay and loss. Simulation experiments are conducted to test the performance of the proposed mechanisms. We assume a three-state Markovian channel model and study the performance with and without retransmissions. We show that the feedback-based technique coupled with retransmissions, aggregation, and variable length MPDUs are effective and increase the R-score and mean opinion score by about 40 percent.

The IEEE 802.22 standard for wireless regional area network is the first standard for cognitive radio that tries to harness the idle or under-utilized spectrum allocated for TV bands. Two major challenges that are faced by IEEE 802.22 are (i) the issue of self co-existence and (ii) the hidden incumbent problem. In this paper, we discuss these two challenges and provide enhancements to the existing IEEE 802.22 air-interface. We use a graph theoretic technique and propose utility graph coloring for allocating spectrum to different IEEE 802.22 base stations so that they can co-exist. The allocation is done such that objectives such as throughput maximization, proportional fairness, and complete fairness are met. We also propose the use of dynamic multiple broadcast messages that resolves the contention among various consumer premise equipments and alleviates the hidden incumbent problem. Through simulation results, we show that the proposed techniques increase the system spectrum utilization and reduce connection set-up delay.

An interesting feature of the 3G cellular networks is their ability to support multiple data rates. Though the performance of TCP over wireless networks has been well studied, there is still no clear understanding on the expected throughput of TCP over multi-rate cellular systems. In this work, we consider a multi-rate system like High Data Rate (HDR) and represent the state space using a Markov chain. We calculate the state transition probabilities assuming transitions are possible between adjacent states only. We calculate the expected data rate conditioned to the initial state both analytically and through simulations. We use an M/G/l queuing model to capture the delay and TCP throughput for each of the initial states. We also illustrate how TCP throughput is affected by parameters like velocity, bit error rate, window size, TCP loss rate, and the underlying radio link protocol.

A vehicular ad hoc network (VANET) is a high mobility wireless ad hoc network that is targeted to support vehicular safety, traffic monitoring, and other commercial applications. Routing in large scale VANET is an important issue, because road complexity and traffic variety may bring many potential problems that existing routing protocol can not address. In this paper, we introduce a two-phase routing protocol (TOPO) that incorporates map information in routing. The proposed protocol defines an overlay graph with roads of high vehicular density and forwards packet along the pre-calculated path in the overlay. We argue that the proposed TOPO can serve as a framework integrating existing VANET routing protocols. In particular, we adopt modified AODV and GPSR in the access (residential area) to send packets to overlay, while greedy forwarding is used in overlay. We also consider the data diversity in VANETs and design TOPO as an ITS friendly protocol. To validate our design philosophy and the routing protocol, we use real world road layouts in Orlando, and generate vehicular mobility traces following some mobility models. We feed the traces to network simulator and study the routing behavior. Simulation results show the performance and effectiveness of our proposed routing protocols for large scale VANET scenarios.

In this paper, we argue that the roaming functionalities implemented at the data link layer (Layer 2) have certain drawbacks causing delay in the handoff process for mobile hosts in a wireless local area network. Through real experiments, we show how the triggering of the roam process is delayed which affects data transmission. Even though the network interface cards and drivers were from different manufactures, the decision process related to the hand-offs had significant similarities - the use of a crude timer and an un-sophisticated approach for detecting error conditions. Identifying the mechanisms that cause a mobile host to roam, we suggest a pro-active algorithm that eases the effects of waiting too long to roam. By monitoring the current received signal strength, we were able to initiate the probing process early enough which in turn helped the roaming process.

In this paper, we focus on the problem of routing in resource rationed ad hoc networks. We exemplify the concept of resource rationing in a military scenario where the allocated resources are rationed to prolong the mission. By modeling the network as a directed weighted graph, we propose an economic approach to address the routing issue, namely HR3. Our approach begins with a hierarchical bidding process, through which nodes in the network bid for virtual currency. The winning bids use the granted virtual currency to pay the intermediate nodes for packet forwarding. After successful delivery, nodes get rewards. We study the truthfulness of HR3with rational nodes and propose security extension to identify and isolate the malicious nodes. Through simulations on ns-2, HR3shows performance gains in data throughput and energy consumption.

Opportunistic scheduling algorithms are effective in exploiting channel variations and maximizing system throughput in multi-rate wireless networks. However, most scheduling algorithms ignore the per-user quality of service (QoS) requirements and try to allocate resources (e.g., the time slots) among multiple users. This leads to a phenomenon commonly referred to as the exposure problem wherein the algorithms fail to satisfy the minimum slot requirements of the users due to substitutability and complementarity requirements of user slots. To eliminate this exposure problem, we propose a novel scheduling algorithm based on two-phase combinatorial reverse auction with the primary objective to maximize the number of satisfied users in the system. We also consider maximizing the system throughput as a secondary objective. In the proposed scheme, multiple users bid for the required number of time slots, and the allocations are done to satisfy the two objectives in a sequential manner. We provide an approximate solution to the proposed scheduling problem which is NP-complete. The proposed algorithm has an approximation ratio of (1 + logm) with respect to the optimal solution, where m is the number of slots in a schedule cycle. Simulation results are provided to compare the proposed scheduling algorithm with other competitive schemes.

In this paper, we propose a unified framework for rate allocation and admission control for CDMA data networks with a goal of maximizing the service provider's revenue taking into consideration user churn behavior, i.e., users' migration from one provider to another. The power constrained discrete rate allocation (PC-DRA) problem that finds the rates at which the base station must transmit to each user is very difficult because of the nonlinear nature of the objective function, the discrete nature of available rates, and the complex relationship between data rate, the target SINR, and the total power constraint. We prove that the PC-DRA problem is NP-complete for CDMA data networks and then propose three heuristics - a two step selective rate reduction (SRR) scheme, a genetic algorithm (GA), and an improved genetic algorithm (IGA) to solve it. We show that all three heuristics can achieve suboptimal that is very close to the global optimal, while SRR provides solutions of high quality with much less computation time than GA and IGA. At the frame level, the rate allocation maximizes the service provider's revenue by adjusting individual user's data rate on a frame by frame basis. On the other hand, the service provider's revenue varies with the system load, which is decided by admission control at the session level. Thus, the admission control needs to maintain an optimal system load. An efficient resource management framework is provided by the cross level (layer) interaction between the link layer (rate control) and the network layer (admission control) in the sense that 1) they share the same objective of maximizing the service provider's revenue, and 2) the solution to the rate allocation problem provides critical information for the admission control decision. The proposed framework is evaluated by extensive simulation experiments with realistic Web browsing and FTP traffic models. Simulation results confirm that the framework is able to provide class-based differentiated data services that also maximize the service provider's revenue

The growing popularity of wireless network has prompted the service providers to look for appropriate ways to meet the demand while recovering the invested capital. This calls for a good revenue model, where pricing of services plays an important role. This paper investigates the performance of smart market based pricing models for wireless network services. Conventionally, these models have been successfully applied in various other service fields including wired networks. In particular, we apply the French auction and Dutch auction models and compare their performances in comparison with the flat-pricing scheme, which is quite popular in the wireless domain. Simulation results on the performance the proposed schemes, with respect to the total revenue generated and the call blocking probability, show encouraging results. © 2006 IEEE.

In this paper, we exploit the flexible features in the medium access control (MAC) layer of WiMax for construction and transmission of MAC protocol data units (MPDU) for supporting multiple VoIP streams over a WiMax link. Quality of VoIP calls, usually given by R-score, is studied with respect to delay and loss of packets. We observe that loss is more sensitive than delay, hence we trade delay for loss. We propose a combination of techniques that exploit the flexibility of the WiMax MAC layer to strike a balance between loss and delay. These techniques are forward error correction, automatic repeat request, MPDU aggregation, and minislot allocation. Simulation experiments are conducted to test the performance of the proposed mechanisms. We assume a three-state Markovian channel model and study the performance with and without retransmissions. We show that the feedback-based technique coupled with retransmissions, aggregation, and variable length MPDUs are effective and increases the R-score by about 40%.

In this paper, we study the performance of a routing algorithm in a CDMA-based sensor network with respect to three energy metrics. These commonly used metrics are expected lifetime, multiple access interference, and queue length - all of which provide insight into how energy is depleted when nodes participate in packet forwarding. We formalize these metrics and show how the delay, throughput, and node life time are affected when routing is performed with a specific objective. Through ns-2 based simulation experiments, we show the tradeoffs between network performance and the different routing strategies adopted. These strategies can also be combined in order to achieve a desired system performance

A substantial portion of the emerging wireless data service consists of non-real-time applications such as content download. The existing mechanisms based on per-packet performance guarantees used mainly for voice and streaming media do not suffice for the elastic nature of non-real-time traffic. For a non-real-time user data services, the key performance measure of interest is the total download time. In this paper, we propose a novel scheduling framework for wireless content service. Specifically, we present a two-layer scheduling architecture that combines content-aware scheduling with opportunistic scheduling. In terms of content-awareness, the proposed scheduling policy provides guarantees on the download time of content. In the second stage, the instantaneous channel conditions of different users are exploited in an opportunistic manner so as to maximize the throughput of the system. We define service differentiation in two modes - differential and guaranteed - and provide polynomial time algorithms for both that manipulate the stretch ratio but within allowable limits. Extensive simulations are conducted that verify the efficiency of the proposed schemes and provide insights into the behavior of the scheduling algorithms for non-real-time data.

To bring TCP-based services to the mobile devices in a cellular network, it is necessary that TCP be extended over the wireless link. However, the performance of TCP severely degrades in a wireless medium. Hence, radio link protocols (RLPs) are used as an interface between TCP and the physical medium. RLPs fragment TCP segments into frames and use robust error correcting codes and fast retransmission schemes to shield the channel related losses from TCP, thus preventing TCP throughput degradation. In this paper, we show the limitations of the existing RLPs, which do not differentiate the frames generated from the same TCP segment. We claim that if selective frames are made more robust to transmission failures, then the performance of RLP and, hence, TCP can be improved. We identify such decisive frames and categorize them as crucial and noncrucial. Our claim is based on the fact that initial frames can afford a few trials of retransmissions, whereas the later ones cannot. We treat the frames differentially with respect to forward error correcting (FEC) coding and automatic repeat request (ARQ) schemes. We consider specific cases of FEC and ARQ strategies and show the qualitative difference in the performance of the RLP through analysis and simulations. The gain in the performance is more prominent when both FEC and ARQ (hybrid-ARQ) are used. The increase in TCP throughput with the proposed RLP is also demonstrated

Multipath routing algorithms in sensor networks provide extensive scope for adaptively routing and dispersing traffic over the network. An essential requirement for adaptive routing in sensor networks is to account for the high cost associated with the use of battery power and network bandwidth. In this paper, we propose an adaptive multi-path routing algorithm (AMRA) that diffuses the traffic such that energy consumption in minimized. We define a Hamiltonian as a function of the cost incurred and the benefit obtained by a node for forwarding packets. The Hamiltonian is then subjected to Pontryagin's cost minimization for efficiently diffusing the packets among the next-hop nodes. This traffic diffusion is computed by solving the steady state queue length equation which we represent in the frequency domain, ns-2 based simulation experiments are conducted and the performance of the proposed algorithm is compared against the AODV routing protocol.

The success of future generation wireless data services depends on the parameterized provisioning of quality of service (QoS) for applications whose demands and nature are highly heterogeneous. Also, user satisfaction plays a key role in the economic viability of wireless service deployments. In this paper, we present a QoS framework based on the paradigm of traffic class and user satisfaction. The proposed class-based QoS framework comprises a radio resource management scheme which considers user satisfaction based on the perceived QoS, and caters to heterogeneous applications such as voice and multiple types of data services, with diverse QoS requirements. The resource management scheme has two components: the admission control algorithm caters to the long term user satisfaction while the session-based rate and bandwidth allocation scheme manipulates the short term user satisfaction. Performance metrics have been specifically defined for each traffic class - blocking for voice communications, average rate jitter for streaming applications, bounded and negotiated delay for interactive data and background traffic. Extensive simulations using four types of traffic and three classes of users reveal that the proposed framework offer improved QoS without compromising the utilization of the system.

Non-uniform coverage and low system throughput due to location dependent fading in cellular networks is a major bottleneck in providing quality of service. Moreover, the growing number of stand-alone Wi-Fi hotspots is creating competition for 3G cellular networks. We propose an architecture which augments the existing 3G downlink technology with Wi-Fi like relay nodes leveraging benefits of both forms to improve the 3G system performance and provide uniform coverage throughout the cell. The relay nodes in the proposed WRN (Wi-Fi like relay network) architecture are equipped with a dual radio interface to communicate with the base station and other relay nodes. We compare the performance of the existing CDMA/HDR-based 3G cellular network with the proposed WRN architecture in terms of throughput and blocking probability. We demonstrate how pre-engineered deployment of relay nodes can yield better performance than random deployment of relay nodes. Simulation experiments are conducted to corroborate our analytical findings. The system throughput is not only enhanced, but the fairness among users is also maintained.

The IEEE 802.16 standard (commonly known as WiMAX), which has emerged as a broadband wireless access technology, is capable of delivering very high data rates. However, providing performance guarantees to delay sensitive applications like streaming media is still a challenge. We study the media access control (MAC) layer of WiMAX and exploit its flexible features to construct MAC packet data units (MPDU) dynamically. The sizes of the MPDUs are constantly modified based on channel state information. The desired payload is obtained either by aggregation or fragmentation of the upper layer data units. The robustness of MPDUs is also made tunable by means of cyclic redundancy code bits. We consider both the scenarios - with and without feedback. We adhere to the 802.16 specifications and propose adapting the MPDU length for streaming media for better performance. Three metrics are defined: restore probability; goodput; dropping probability. Simulation experiments are conducted which show the performance enhancements of the proposed ARQ-enabled adaptive algorithm in terms of these three metrics.

In this paper, we study the reliability as offered jointly by the medium access control (MAC) and the radio link protocol (RLP) for third generation (3G) code division multiple access (CDMA) standards. The retransmission mechanism supported at the RLP layer has a considerable amount of delay (80-100 ms) associated with it; hence, it may not be able to support applications with strict delay requirements. On the other hand, if retransmissions are also performed at a lower layer, such as the MAC layer, then the performance of CDMA systems can be improved because of the fast retransmissions at the MAC layer. We show how the performance of RLP improves with respect to three metrics - mean delay, throughput, and RLP recovery -when a finite number of retransmissions is allowed at the MAC layer. Synthetically generated Web traffic is used as the application, the objects of which are fragmented into equal-sized RLP frames. We consider soft packet combining support at the receiver, which effectively lowers the frame error rate. We also consider the possibility of misinterpretations of acknowledgments. Simulation experiments are conducted to verify the performance as offered jointly by the MAC and the RLP, particularly for cdma2000 standard and wideband CDMA (WCDMA) systems. The improvement in the TCP throughput is also evaluated.

The competition among wireless data service providers brings in an option for the unsatisfied customers to switch their providers, which is called churning. The implementation of Wireless Local Number Portability (WLNP) is expected to further increase the churn rate (the probability of users switching the provider). However, the existing resource management algorithms for wireless networks fail to fully capture the far-reaching impact of this unforeseen competitiveness. From this perspective, we first formulate noncooperative games between the service providers and the users. A user's decision to leave or join a provider is based on a finite set of strategies. A service provider can also construct its game strategy set so as to maximize their utility (revenue) considering the churn rate. Based on the game theoretic framework, we propose an integrated admission and rate control (ARC) framework for CDMA-based wireless data networks. The admission control is at the session (macro) level while the rate control is at the link layer packet (micro) level. Two admission control modes are considered - one-by-one mode and batch processing mode, in which multiple users are admitted at a time. We show that: 1) for the one-by-one mode, the Nash equilibrium using pure strategy can be established for both under-loaded and fully-loaded systems and 2) for batch processing mode, there is either an equilibrium in pure strategy or a dominant strategy exists for the service provider. Therefore, the providers have clearly defined admission criteria as outcome of the game. Users are categorized into multiple classes and offered differentiated services based on the price they pay and the service degradation they can tolerate. We show that the proposed ARC framework significantly increases the provider's revenue and also successfully offers differentiated QoS to the users.

We propose enhancements to radio link protocols by identifying decisive frames and categorizing them as crucial and non-crucial. The fact that initial frames from the same upper layer segment can afford a few trials of retransmissions and the later frames cannot, motivates our work. We treat the frames differentially with respect to FEC coding and ARQ schemes. We consider specific cases of FEC and ARQ strategies and qualitatively show how the differential treatment of frames can improve the performance of the RLP.

In this paper, we characterize analytically the multiaccess interference (MAI) in wireless CDMA sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of the signature code length and the receiver design on network performance for different topologies is also studied.

Non-real time content delivery constitutes a major portion wireless data services in 3G networks. The per-packet performance guarantees required for real-time applications, such as voice and streaming media, is achieved through conventional flow-based scheduling mechanisms. However, for data services, such scheduling mechanisms cannot be used since the key performance measure for non-real time applications is the total download time. In this paper, we present a two-layer scheduling architecture that combines content-aware scheduling with opportunistic scheduling. In terms of content-awareness, the proposed scheduling policy provides guarantees to the download time of a content. Furthermore, through parallelizing the schedule and using opportunistic channel usage, the scheme achieves higher throughput. Extensive simulations verify the efficiency of our proposed schemes and provide insights into the behavior of the scheduling algorithms.

The deployment of 3G cellular networks is ushering in a variety of data services in conjunction with the existing voice services. A major proportion of data services will include Web browsing and file downloads categorized as nonreal time services, for which there exists no service level agreement between the users and the providers on the quality of data services. In this paper, we address how to deal with the subjectiveness of user satisfaction or expectation from service providers by defining user irritation factors with the help of Sigmoid functions. These factors reflect users' sensitivity and tolerance to delay. We propose a scheduling algorithm to provide a bound on the delivery time of the entire data content. This algorithm takes into consideration the dynamic nature of the irritation factors, channel conditions, user classes, and fairness within individual classes. Soft-reservation schemes are also proposed to cater to the higher paying users. Exhaustive simulations are conducted with synthetically generated HTTP traffic and three classes of users. Experimental results demonstrate that the proposed scheme is able to provide bounded delay even when the system utilization is as high as 80%.

Integrating the two successful domains, viz, cellular networks and the Internet, to have anytime, anywhere access is one of the objectives of third generation (3G) cellular networks. In this paper a hierarchical architecture for the integration of cellular network (GSM) and mobile IPv6 as mobility management is proposed. GSM is the most widely used cellular network and mobile IP allows transparent routing of IP datagrams in the Internet to the mobile node irrespective of its physical location. Mobile IP provides an elegant solution for inter-domain, or macro-mobility management, but does not perform well for micro-mobility or intra-domain management. The transition of IPv4 to IPv6 is studied and its improvements are incorporated in the proposed architecture. The proposed hierarchical architecture improves the performance of mobile IP in GSM networks in terms of fast intra-domain hand-offs and reduced latency.

In this paper, we characterize analytically the multiaccess interference in wireless code-division multiple-access sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of signature code length and receiver design on the network performance for different topologies is also studied.

In this paper, we provide a game theoretic approach for maximizing the utility of a carrier in a competitive market. Due to the deregulation of the telecommunications industry there is more than one service provider in any region. This gives the users the flexibility to switch their provider if they are not satisfied with their service. In this perspective, we propose a utility function based on non-cooperative game which considers the probabilities of users leaving the network. We categorize users into three classes who get differentiated services based on the price they are willing to pay. The game theoretic approach allocates resources both during service admission and burst admission. The utility function considered is from the service provider's point of view. Our model demonstrates the impact of admitting high paying users on other users.

The competition among wireless data service providers brings in an option for the customers to switch their providers, due to unsatisfactory service or otherwise. However, the existing resource management algorithms for wireless networks fail to fully capture the far-reaching impact of this competitiveness. From this perspective, we propose an integrated admission and rate control (ARC) framework for CDMA based wireless data networks. The admission control is at the session (macro) level while the rate control is at the link layer packet (micro) level. The ARC framework is based on a novel game theoretic formulation which defines non-cooperative games between the service providers and the customers. A user's decision to leave or join a provider is based on a finite set of strategies. A service provider can also construct its game strategy set so as to maximize the utility (revenue) yet attaining its target churn rate (the probability of users leaving the network). We show that the pure strategy Nash equilibrium can be established for both under-loaded and fully-loaded systems such that the providers have clearly defined admission criteria as outcome from this game. Users are categorized into multiple classes and offered differentiated services based on the price they pay and the service degradation they can tolerate. We show that the proposed ARC framework significantly increases the provider's revenue and also successfully offers differentiated QoS to the users.

This paper deals with the performance modeling of the various MAC states as defined by the cdma2000 protocol. Our method uses a composite performance metric which has the capability of proportionally combining three basic parameters: channel utilization, waiting time and the saving in the signalling overhead. The scheduler at the base station is not only responsible for admitting new services into the system but also for switching the MAC states of a service depending on its activity. Since the true nature of the wireless data traffic is yet unknown, we use a mix of Poisson-distributed voice packets and Pareto-distributed data packets. We derive analytical expressions and also conduct simulation experiments to study the nature of the performance curve and thus compute the optimal values of expiration timers at which the MAC states should be switched such that the system performance is maximized. We show how our model can be made suitable for different systems by tuning the scaling functions (or weights) for each of the three performance parameters considered.

We study the joint reliability as offered by the medium access control (MAC) and the radio link protocol (RLP) in CDMA systems, particularly for 1XTREME. The retransmission mechanism in the RLP has considerable amount of delay associated with it which might not be able to sustain real-time communications with strict delay requirements. Retransmissions done at a lower layer, such as the MAC, enhances the performance of the system. We show how the performance varies with respect to delay and throughput as observed from the RLP, for finite number of retransmissions at the MAC. We find that there is a substantial gain in the performance with the incorporation of fast retransmissions at the MAC. Synthetically generated HTTP traffic was used as the application, the objects of which were fragmented into equal sized RLP frames. We consider an I-path Rayleigh fading channel with correlated errors. We also consider that soft packet combining at the receiver is supported, which effectively lowers the frame error rate (FER). Simulation experiments are conducted to verify the performance as offered by the MAC and the RLP.

We study the reliability as offered jointly by the medium access control (MAC) and the radio link protocol (RLP) for CDMA systems, particularly for the cdma2000 evolution standard cdma2000 1-EV. The retransmission mechanism in the RLP has a considerable amount of delay associated with it which might not be able to sustain real-time communications with strict delay requirements. Retransmissions done at a lower layer, such as the MAC, enhances the performance of the system. We show how the performance varies with respect to delay and throughput as observed from the RLP, for finite number of retransmissions at the MAC. We also consider that soft packet combining at the receiver is supported, which effectively lowers the frame error rate (FER). Simulation experiments have been conducted where synthetically generated HTTP traffic was used as the application, the objects of which were fragmented into equal sized RLP frames before being transmitted over the wireless channel. We observe that the the delay and the RLP retransmissions are low enough to sustain real-time communications with the incorporation of fast retransmissions at the MAC layer.

We present an adaptive service (or call) admission control algorithm (ASACA) for integrated services packet networks. The proposed algorithm does not require a priori knowledge of the source characteristics, and it consists of two steps. The first step adapts the service rate of the system based on the number of frames lost during the observation period, and the second step estimates the expected delay associated with each class of service upon admitting or rejecting a service in the system. For this purpose, we use an M/M/1 queueing model such that the current statistical delay is added to the error estimates of the delay scaled by its gradient. While admitting a service, we ensure that the delay commitments towards the requirement profile of ongoing services are preserved. Further the sum of the statistical arrival rates for each class of service and the peak rate of the new call is not allowed to exceed the current system service rate. Simulation experiments demonstrate that our adaptive service control algorithm, ASACA, performs twice as well as a non-adaptive algorithm using an M/M/1 model, in meeting the service requirements but at the cost of a reduced number of calls admitted into the system, the number of which depends on the system load.

We consider a multi-cluster, multi-hop packet radio network architecture for wireless systems which can dynamically adapt itself with the changing network configurations. Due to the dynamic nature of the mobile nodes, their association and dissociation to and from clusters perturb the stability of the system, and hence a reconfiguration of the system is unavoidable. At the same time it is vital to keep the topology stable as long as possible. The clusterheads, which form a dominant set in the network, decide the topology and are responsible for its stability. In this paper, we propose a weighted clustering algorithm (WCA) which takes into consideration the ideal degree, transmission power, mobility and battery power of a mobile node. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol, Our clusterhead election procedure is not periodic as in earlier research, but adapts based on the dynamism of the nodes. This on-demand execution of WCA aims to maintain the stability of the network, thus lowering the computation and communication costs associated with it. Simulation experiments are conducted to evaluate the performance of WCA in terms of the number of clusterheads, reaffiliation frequency and dominant set updates, Results show that the WCA performs better than the existing algorithms and is also tunable to different types of ad hoc networks.

The advent of powerful hand-held computers and the desire for communication on the move, are the driving forces behind an emerging technology called mobile computing. Observing the growing demands of roaming users, it has been predicted that the next generation wireless networks will be burdened with the bandwidth-intensive traffic generated by personal multimedia applications. However, the available bandwidth for supporting these applications is still limited, and therefore its proper management is necessary to ensure the required quality of service (QoS) provisioning between the end-systems. We first present a survey of the existing literature on the QoS provisioning for wireless networks, which mainly focus on identifying the technological bottlenecks involved. Then we propose a unified framework to provide a link layer as well as a network layer solution for QoS provisioning. Our framework measures two important QoS parameters, namely the inter-packet delay and the transmission data rate. Experimental results show as high as a 21% improvement in the call admission probability and about a 16% improvement in the hand-off call dropping probability.