Assessment of autoencoders is frequently constrained to average empirical losses, which lack principled and distributional guarantees. Existing methods incorporating optimal transport are training-centric and fail to provide post-hoc certificates for a trained model. In this paper, we derive a high-probability, data-dependent upper bound on the Wasserstein-1 distance between the input and reconstruction distributions. The bound is computed directly from the empirical mean absolute error on a hold-out set, requiring neither retraining nor the use of an optimal transport solver. We provide an explicit finite-sample deviation radius scaling. Moreover, we introduce an analytic attribution framework based on a joint-Gaussian surrogate, which provides interpretable insight into the structure of the reconstruction error. This framework decomposes the expected reconstruction loss into bias, variance, and alignment components in closed form. This enables an auditable procedure for both certifying and diagnosing autoencoder reconstructions.

To alleviate network congestion resulting from packet retransmission in Federated Learning (FL) systems, the User Datagram Protocol (UDP) has been adopted. However, data loss under UDP inevitably degrades FL performance, as learning is sensitive to incomplete model parameters. This paper addresses such degradation by proposing a low-rank approximation–based parameter transmission method for FL. This approach decomposes model parameters at the transmitter to extract dominant singular values, which are essential for accurate approximation of the model parameters at the receiver. Although the selective delivery of singular values and vectors reduces communication overhead, their loss would cause severe performance degradation, making it essential to employ protection mechanisms. Therefore, to protect the extracted singular values and vectors, we use Systematic Network Coding (SysNC). The SysNC with low-rank approximation can recover original information under a packet loss environment, enhancing robustness against partial loss during the transmission of model parameters. Theoretical analysis and experiments confirm its effectiveness. As an example from our experiments, with a packet loss ratio of p = 0.1, the proposed method achieves over 96% of the accuracy observed in the packet loss-free case, while reducing the end-to-end delay for model parameter transmission by approximately 50% compared to a UDP baseline.

In federated learning (FL), it is essential to implement a payment allocation mechanism that compensates clients for the costs incurred from participating in FL tasks. In this letter, we formulate the payment allocation as a bargaining game between a global server and clients and adopt the Nash bargaining solution (NBS) to achieve optimal and fair payment assignments among clients. Unlike existing payment allocation mechanisms that require the disclosure of private information from the clients, the proposed approach ensures privacy non-disclosure for bargaining. The key idea is to decompose the one-to-many bargaining game into independent one-to-one bargaining games and use alternating-offers, which do not require the disclosure of private information from clients. We design an alternating-offers strategy and acceptance criteria to ensure fair agreements without the private information of clients. Simulation results show that the proposed payment allocation strategy can fairly allocate payments to clients while maintaining the accuracy of the global server in FL tasks.

In this paper, we present a novel approach of symmetrical pruning for lightweight anomaly detectors based on an autoencoder, leveraging the unique encoder-decoder structure of the autoencoder. We develop an efficient network anomaly detector with reduced computational overhead by computing the reconstruction error between hidden activations of an input and its hidden reconstructions and symmetrically pruning nodes with high error values.

Data-driven anomaly detection for machines in the wireless Industrial Internet of Things (IIoT) environments is a critical task in modern industrial domains for system stability and reliability. Edge computing in IIoT environments can offer advantages by reducing decision-making time and bandwidth usage. However, edge nodes have stringent constraints in memory and computing capacities. Thus, to implement an anomaly detector on edge nodes, it is necessary to develop efficient data preprocessing algorithms and a lightweight anomaly detection model. The proposed algorithm employs a parallel discrete wavelet transform that can efficiently capture and compress both low and high-frequency content in the acoustic signals, significantly reducing data preprocessing and model training time as well as memory usage. The experimental results, using real-world data collected from industrial machines, confirm the efficient use of memory and computing resources in the edge nodes.

In response to the security threats in wireless networks with concurrent device connections, deploying Intrusion Detection Systems (IDS) at the network edge is a promising strategy. However, this approach must take into account the resource constraints incurred by power-limited edge devices, requiring a lightweight solution to IDS. At the same time, the lightweight IDS solution has to minimize performance degradation as higher detection performance is also a key requirement of IDS. In this paper, we design a lightweight autoencoder with explainability, employing the Shapley value to measure unit importance and link importance. This approach can selectively activate only critical components, thereby reducing the complexity for IDS while effectively lowering its performance degradation. We confirm that the proposed algorithm is robust against the harsh sparsity of the autoencoder. Moreover, the sparsity of the proposed lightweight autoencoder can be easily manageable, such that it can be controlled to satisfy the potential constraints of power-limited edge devices. Therefore, the solution is a suitable algorithm for IDS that can be deployed on edge devices in wireless networks.

In this paper, we propose a low-rank federated learning (FL) algorithm based on singular value decomposition (SVD). The SVD factorizes the global parameters that need to be exchanged between a global server and clients for distributed model training, significantly reducing the associated communication cost. Experiment results confirm that the number of transmissions is significantly reduced while maintaining the accuracy performance of the local model using the approximately recovered parameters.

In this paper, we propose a multilayered Long ShortTerm Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.

In this paper, we propose a bidirectional imputation algorithm for spatio-temporal traffic speed data based on Long Short-Term Memory (LSTM) architecture with parameter transfer in vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where an Road Side Units (RSU) on the road does not operate temporarily and thus the traffic speed data cannot be collected. This makes any services that rely on the traffic speed data at the RSU be unavailable. For uninterrupted and seamless V2I services, an efficient and low complexity data imputation algorithm is imperative. The proposed algorithm is based on the architecture that includes multiple LSTM layers with parameter transfers, which can explicitly take into account the spatio-temporal characteristics of the traffic speed data. By transferring parameters from one LSTM layer to its adjacent LSTM layer, the complexity associated with the algorithm can be significantly reduced. The proposed algorithm includes bidirectional imputation, which can further improve imputation accuracy. Our simulation and experiment results confirm that the time for training and data imputation of the proposed algorithm can be significantly reduced while maintaining imputation accuracy.

In this paper, we aim to find a robust network formation strategy that can adaptively evolve the network topology against network dynamics in a distributed manner. We consider a network coding deployed wireless ad hoc network where source nodes are connected to terminal nodes with the help of intermediate nodes. We show that mixing operations in network coding can induce packet anonymity that allows the inter-connections in a network to be decoupled. This enables each intermediate node to consider complex network inter-connections as a node-environment interaction such that the Markov decision process (MDP) can be employed at each intermediate node. The optimal policy that can be obtained by solving the MDP provides each node with the optimal amount of changes in transmission range given network dynamics (e.g., the number of nodes in the range and channel condition). Hence, the network can be adaptively and optimally evolved by responding to the network dynamics. The proposed strategy is used to maximize long-term utility, which is achieved by considering both current network conditions and future network dynamics. We define the utility of an action to include network throughput gain and the cost of transmission power. We show that the resulting network of the proposed strategy eventually converges to stationary networks, which maintain the states of the nodes. Moreover, we propose to determine initial transmission ranges and initial network topology that can expedite the convergence of the proposed algorithm. Our simulation results confirm that the proposed strategy builds a network which adaptively changes its topology in the presence of network dynamics. Moreover, the proposed strategy outperforms existing strategies in terms of system goodput and successful connectivity ratio.

Joint transmission, included in coordinated multipoint scheme, can improve the throughput of cell edge users by controlling transmit power of cooperating base stations. This, however, may cause another interference with inappropriate power allocation. In this paper, we model the power allocation of base stations as a game. The cooperating base stations are defined as players in the game and they allocate their power to maximize their utility. In the process, we consider the power constraints of cooperating base stations and the cost factor that reflects the degree of interference in other non-cooperating base stations. We show that there exists a Nash equilibrium in this power allocation game and the simulation results confirm that the proposed approach leads to an equilibrium.

In this paper, we propose a distributed solution based on game-theoretic approaches to the topology formation problem for mobile wireless sensor networks with multi-source multicast flows. Our solution significantly reduces computational complexity by taking advantage of network coding. Finding an optimal topology for network coding in multi-source multicast flows is NP-hard problem, so the proposed algorithm provides a suboptimal solution with low computational complexity. We formulate the problem of distributed network topology formation as a network formation game by considering the nodes in the network as players that can take actions for making outgoing links. The proposed game, which consists of multiple players and multicast flows, can be decomposed into independent link formation games played by only two players with a unicast flow. The proposed algorithm is also guaranteed to converge, i.e., a stable network topology can be always formed. Our simulation results confirm that the computational complexity of the proposed solution is low enough for practical deployment in large-scale mobile, wireless sensor networks.

In this letter, we propose a distributed network formation solution for network coding deployed wireless networks which includes multisource multicast flows. This is an attempt to solve an open problem of network coding based multisource multicast flow design based on a game theoretic approach, which can eventually form a network in a distributed way. The network is in particular constructed by individual decision makings of the nodes, while taking advantages of network coding techniques. The decisions made by the nodes include the transmission powers and the use of network coding operations. In each stage game, nodes update the parameters based on feedbacks such as rewards, penalties, and evaluate their prior actions, which enables the nodes to make best responses in the next stage game. Our simulations confirm that the resulting network can reduce overall power consumption compared to direct transmission, and improve system throughput with less power consumption compared to no coding strategy.

We propose a practical system design for biometrics authentication based on electrocardiogram (ECG) signals collected from mobile or wearable devices. The ECG signals from such devices can be corrupted by noise as a result of movement, signal acquisition type, etc. This leads to a tradeoff between captured signal quality and ease of use. We propose the use of cross correlation of the templates extracted during the registration and authentication stages. The proposed approach can reduce the time required to achieve the target false acceptance rate (FAR) and false rejection rate (FRR). The proposed algorithms are implemented in a wearable watch for verification of feasibility. In the experiment results, the FAR and FRR are 5.2% and 1.9%, respectively, at approximately 3 s of authentication and 30 s of registration.

In this paper, we present an optimal charging algorithm for multiple devices in the perspective of user satisfaction fairness. The proposed algorithm can maximize the total sum of the increment of user satisfaction levels in a finite charging scheduling period. In order to develop the algorithm, we define an objective function that includes the characteristics of charging capacity behaviors for the associated devices and formulate the problem as a convex optimization problem. Therefore, the proposed algorithm can be efficiently implemented in a very low complexity. Simulation results confirm that the proposed algorithm can achieve the maximum sum of the increment of user satisfaction levels compared to existing conventional approaches (e.g., equal allocation), thereby leading to an improved performance.

In this paper, we consider a delay-sensitive data transmission strategy based on network coding technique in finite fields over error-prone networks. In order to solve all-or-nothing problem inherited from network coding, compressed network coding is proposed by jointly considering network coding techniques and compressed sensing technique. While network coding techniques have been jointly used with the compressed sensing techniques, network coding operations are performed in the field of real numbers, and thus, the payload of transmitted data can be enlarged as the data traverse more hops in networks. In this paper, however, we propose to use network coding techniques in finite fields, such that the size of payload does not increase as more hops are traversed. With the help of compressed sensing technique, a destination node is able to approximately recover the source data based on l1-norm minimization approach, in case of innovative packet loss. It is analytically shown that the payload size of the proposed approach is always smaller than that of the conventional approach, while the proposed approach can achieve comparable decoding performances. We evaluate the effectiveness of the proposed approach based on an illustrative application of image delivery system.

We propose a sensor network based novel strategy for BEMS (Building Energy Management System). The strategy is to minimize the total cost of energy for a finite period by efficiently controlling energy flows with predicting and monitoring those flows based on the sensor network in buildings. The proposed strategy includes prediction, long-term scheduling, and prediction error update within a building. During the period, the process from the prediction to the update is iterated in every time unit when the system status is changed by a dynamic environment. The scheduler determines the optimal energy flows based on the prediction, and the updated error by a dynamic environment is finally fed back for the next iteration. Simulation results indicate potential cost savings that are approximately 10∼20% compared to a typical BEMS with a conventional RTC (real-time control) scheme.

Efficient resource management strategies are important for multiuser multimedia applications, as they are often serviced over resource-constrained and shared network infrastructure. Moreover, an acceptable level of quality e.g., Quality of Service (QoS) should be guaranteed. In this paper, we consider a game-theoretic resource management strategy, where the bargaining solutions are deployed in the resource allocation. We are in particular interested in the Nash Bargaining Solution (NBS) that can allocate resources in a fair and optimal way, while explicitly considering the achieved utility. Finding the NBS, however, is a challenging task due to its potentially high computational complexity, especially when a large number of users and the large amount of resources are available. In order to overcome the problem, we propose an iterative approach that requires significantly lower computational complexity compared to the conventional approach. The proposed approach decomposes the bargaining problem into sub-bargaining problems, where a sub-bargaining problem considers smaller feasible set and computes the corresponding sub-NBS. This step is iteratively repeated for successive sub-bargaining problems until the NBS is obtained. We show that the proposed sub-NBS approaches the NBS with a small error while significantly reducing the complexity required to find the NBS.

This paper proposes a robust decoding algorithm in delivery of network coded data which is in particular correlated and delay-sensitive. We consider ad-hoc sensor network topologies, where a correlated data is delivered based on network coding techniques in conjunction with approximate decoding algorithm in order for efficient and robust data delivery. The approximate decoding algorithm has been developed as a decoding solution to ill-posed problems for network coded correlated data sources. In this paper, we improve the performance of approximate decoding algorithm by explicitly considering more information, which is used to additionally refine the recovered data. The information includes potential results that are from finite field operations and the set of such information is referred to as position information matrix in this paper. We deploy the position information matrix into approximate decoding algorithm and investigate its corresponding properties. We then analytically show that this improves the performance of approximate decoding algorithm. Our simulation results confirm the properties of the proposed approximate decoding algorithm with position information matrix and improved performance.

We propose a BitTorrent-like protocol based on an online learning (reinforcement learning) mechanism, which can replace the peer selection mechanisms in the regular BitTorrent protocol. We model the peers' interactions in the BitTorrent-like network as a repeated stochastic game, where the strategic behaviors of the peers are explicitly considered. A peer that applies the reinforcement learning (RL)-based mechanism uses the observations on the associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations such that the peer can maximize its long-term performance. We have implemented the proposed mechanism and incorporated it into an existing BitTorrent client. Our experiments performed on a controlled Planetlab testbed confirm that the proposed protocol 1) promotes fairness and provides incentives to contributed resources, i.e., high capacity peers improve their download completion time by up to 33 percent, 2) improves the system stability and robustness, i.e., reduces the peer selection fluctuations by 57 percent, and (3) discourages free-riding, i.e., peers reduce their uploads to free-riders by 64 percent as compared to the regular BitTorrent protocol.

In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning based mechanism. The inherent operation of P2P systems, which involves repeated interactions among peers over a long time period, allows peers to efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding (FR), while improving fairness. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer that applies the reinforcement learning based mechanism uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance.

Network coding has been recently proposed as an efficient method to improve throughput, minimize delays and remove the need for reconciliation between network nodes in distributed streaming systems. It permits to take advantage of the path and node diversity in the network when the network coding nodes are placed efficiently. In this paper, we investigate networks consisting of nodes that autonomously determine whether they should perform network coding or not as well as their set of parent nodes. Each node makes its decisions that maximize its quality of service. The decisions include the selection of operation mode (i.e., network coding mode, simple data forwarding mode) and the selection of extra connections. The resulting interactions among the nodes are modeled as a congestion game, thereby ensuring an equilibrium, i.e., stable multimedia stream flow. The experimental results show that the proposed scheme is appropriate for distributed multimedia transmission since it provides a stable quality without imposing centralized control.

In this paper, we present a solution to efficient multimedia streaming applications over P2P networks based on the foresighted resource reciprocation strategy. We study several priority functions that can explicitly consider the timing constraints and the importance of each data segment in terms of multimedia quality, and successfully incorporate them into the foresighted resource reciprocation strategy. This enables peers to enhance their multimedia streaming capability. The simulation results confirm that the proposed approach outperforms existing algorithms such as tit-for-tat in BitTorrent and BiToS solutions.

Recent research in wireless multimedia streaming has focused on optimizing the multimedia quality in isolation, at each station. However, the cross-layer transmission strategy deployed at one station impacts and is impacted by the other stations, as the wireless network resource is shared among all competing users. Hence, efficient and fair resource management for autonomous wireless multimedia users becomes very important. We consider quality-based fairness schemes based on axiomatic bargaining theory, which can ensure that the autonomous multimedia stations incur the same drop in multimedia quality as compared to a maximum achievable quality for each wireless station. Implementing this quality-based fairness solution in the time-varying channel condition requires high-computational complexity and communication overheads. Hence, we develop solutions that significantly reduce the computational complexity and communication overheads. Our simulations show that the proposed game-theoretic resource management can indeed guarantee desired utility-fair allocations when wireless stations deploy different cross-layer strategies.

In this paper, we consider social peer-to-peer (P2P) networks, where peers are sharing their resources (i.e., multimedia content and upload bandwidth). In the considered P2P networks, peers are self-interested, thereby determining their resource divisions (i.e., actions) among their associated peers such that their utility (e.g., multimedia quality) is maximized. Peers determine their optimal strategies for selecting their action based on a Markov Decision Process (MDP) framework, which enables the peers to maximize their cumulative utilities. We consider heterogeneous peers that have different and limited ability to characterize their resource reciprocations using only a limited number of states. We investigate how the limited number of states impacts the resource reciprocation and the resulting multimedia quality over time. Simulation results show that peers simultaneously refining their state descriptions can improve the multimedia quality in the resource reciprocation. Moreover, peers prefer to interact with other peers that have higher available upload bandwidths as well as have similar capabilities for refining their number of states.

In this paper, we propose a distributed solution to the problem of configuring classifier trees in distributed stream mining systems. The configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of misclassification cost. In the proposed solution, individual classifiers select their operating points (i.e., actions) to maximize a local utility function. The utility may be purely local to the current classifier, corresponding to a myopic strategy, or may include the impact of the classifier actions on successive classifiers in the tree, corresponding to a foresighted strategy. We analytically show that actions determined by the foresighted strategies can improve the end-to-end performance of the classifier tree and derive an associated probability bound. We then evaluate our solutions on an application for hierarchical sports scene classification. By comparing centralized, myopic and foresighted solutions, we show that foresighted strategies result in better performance than myopic strategies, and also asymptotically approach the centralized optimal solution.

We consider the problem of configuring classifier trees in distributed stream mining system. The configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of misclassification cost. We model this as a tree configuration game and design solutions, where individual classifiers select their operating points to maximize a local utility. We derive appropriate misclassification cost coefficients for intermediate classifiers, and determine the information that needs to be exchanged across classifiers, in order to successfully design the game. We analytically show that there is a unique pure strategy Nash equilibrium in operating points, which guarantees a convergence of the proposed approach. We evaluate the performance of our algorithm on an application for sports scene classification, and compare against centralized solutions. We show that our algorithm results in better performance than the centralized solution on average. Moreover, the algorithm approaches the optimal solution asymptotically with increasing number of actions per classifier.

Large-scale multimedia semantic concept detection requires realtime identification of a set of concepts in streaming video or large image datasets. The potentially high data volumes of multimedia content, and high complexity associated with individual concept detectors, have hindered the practical deployment of many current solutions. In this paper, we present a summary of our work in building systems and applications for resource adaptive semantic concept detection in multimedia using large-scale distributed stream mining systems. We construct such concept detection applications as a hierarchical topology of individual concept detectors, and deploy them on distributed processing infrastructure. We then focus on dynamically configuring individual concept detectors to meet system imposed resource constraints while minimizing a penalty defined in terms of the misclassification cost. We present multiple centralized and distributed algorithms for this configuration, and describe the implemented application and system. We also verify through simulations that significant improvement in terms of accuracy of classification can be achieved through our approach.

In this letter, we consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing their content. In the considered P2P system, autonomous and self-interested peers use a Markov decision process (MDP) framework to determine their upload bandwidth allocations, which maximize their individual utilities. This framework enables the peers to make foresighted decisions on their bandwidth allocations, by considering the future impact of their decisions. In this letter, we focus on the impact of the peers' bounded rationality on their resource reciprocation strategies and ultimately, on their achievable utilities. Specifically, we consider peers who have only a limited ability to model the other peers' strategies for resource reciprocation, and study how this impacts their own decisions.

In this paper, we assume that the network resources are managed by several brokers, which are endowed with resources by a (remote) central resource manager according to several predetermined policies. Our focus is on autonomous multimedia users. We propose a novel resource management scheme, where resource brokers choose well-suited axiomatic bargaining solutions to divide their allocated resources among the users associated with them. These resource division solutions enable resource brokers to provide strict minimum video quality guarantees according to the (varying) number of multimedia users associated with them. Finally, we show that the proposed solution enables us to model the problem of selecting resource brokers by multimedia users as an unweighted congestion game, thereby ensuring convergence to a stationary distribution of users across resource brokers. We investigate the number of required users' switches to reach the stationary distribution, and quantify the fairness of the stationary distribution by introducing a novel quality fairness comparison metric for the users.

We consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing multimedia content. In such P2P networks, the shared resources are the peers' contributed content and their upload bandwidth. While sharing resources, autonomous and self-interested peers need to make decisions on the amount of their resource reciprocation (i.e., representing their actions) such that their individual utilities are maximized. We model the resource reciprocation among the peers as a stochastic game and show how the peers can determine optimal strategies for resource reciprocation using a Markov Decision Process (MDP) framework. Unlike existing resource reciprocation strategies, which focus on myopic decisions of peers, the optimal strategies determined based on MDP enable the peers to make foresighted decisions about resource reciprocation, such that they can explicitly consider both their immediate as well as future expected utilities. To successfully formulate the MDP framework, we propose a novel algorithm that identifies the state transition probabilities using representative resource reciprocation models of peers. These models express the peers' different attitudes toward resource reciprocation. We analytically investigate how the error between the true and estimated state transition probability impacts each peer's decisions for selecting its actions as well as the resulting utilities. Moreover, we also analytically study how bounded rationality (e.g., limited memory for reciprocation history and the limited number of state descriptions) can impact the interactions among the peers and the resulting resource reciprocation. Simulation results show that the proposed approach based on reciprocation models can effectively cope with a dynamically changing environment such as peers' joining or leaving P2P networks. Moreover, we show that the proposed foresighted decisions lead to the best performance in terms of the cumulative expected utilities.

Designing efficient and fair solutions for dividing the network resources in a distributed manner among self-interested multimedia users is recently becoming an important research topic because heterogeneous and high bandwidth multimedia applications (users), having different quality-of-service requirements, are sharing the same network. Suitable resource negotiation solutions need to explicitly consider the amount of information exchanged among the users and the computational complexity incurred by the users. In this paper, we propose decentralized solutions for resource negotiation, where multiple autonomous users self-organize into a coalition which shares the same network resources and negotiate the division of these resources by exchanging information about their requirements. We then discuss various resource sharing strategies that the users can deploy based on their exchanged information. Several of these strategies are designed to explicitly consider the utility (i.e., video quality) impact of multimedia applications. In order to quantify the utility benefit derived by exchanging different information, we define a new metric, which we refer to as the value of information. We quantify through simulations the improvements that can be achieved when various information is exchanged between users, and discuss the required complexity at the user side involved in implementing the various resource negotiation strategies.

We consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing content. While sharing resources, autonomous and self-interested peers need to make decisions on the amount of their resource reciprocation (i.e. representing their actions) such that their individual rewards are maximized. We model the resource reciprocation among the peers as a stochastic game and show how the peers can determine their optimal strategies for the actions using a Markov Decision Process (MDP) framework. The optimal strategies determined based on MDP enable the peers to make foresighted decisions about resource reciprocation, such that they can explicitly consider both their immediate as well as future expected rewards. To successfully formulate the MDP framework, we propose a novel algorithm that efficiently identifies the state transition probabilities using representative resource reciprocation models of peers. Simulation results show that the proposed approach based on the reciprocation models can effectively cope with a dynamically changing environment of P2P networks. Moreover, we show that the foresighted decisions lead to the best performance in terms of the cumulative expected rewards.

We propose decentralized solutions for resource negotiation, where multiple autonomous users self-organize into a coalition which shares the same network resources and negotiate the division of these resources by exchanging information about their requirements. We discuss various network resource sharing strategies that the users can deploy based on their exchanged information. Several of these strategies are designed to explicitly consider the utility (i.e., video quality) impact of multimedia applications. To quantify the utility benefit derived by exchanging different information, we define a new metric referred to as the value of information. Simulation results show the improvements that can be achieved when various information is exchanged between users, and discuss the required complexity involved in implementing the various resource negotiation strategies.

With the emergence of shared overlay network infrastructures and the recent deregularization of spectrum policies, a new, more dynamic network resource "market" is emerging. To effectively operate this new market, resource management becomes of paramount importance. This is especially important for multimedia streaming applications that require a large amount of resources to guarantee an acceptable level of multimedia quality to the end users. However, providing the necessary resources to various networked multimedia users is challenging since they have different requirements in terms of multimedia characteristics, delay, or network constraints. To simplify this problem, we propose a novel utility-based resource management scheme for multi-user multimedia transmission over networks. To manage the available resources, the resource manager deploys bargaining solutions from economics in order to explicitly consider the utility impact for different resource allocation schemes. We focus on the Kalai-Smorodinsky bargaining solution (KSBS) because it can successfully model relevant noncollaborative utility-aware fairness policies for multimedia users. The KSBS explicitly considers the application-specific utility domain (i.e., resulting multimedia quality) when performing the resource allocation. The proposed KSBS allocates the resources in such a way that the achieved utility of every participating station incurs the same quality penalty, i.e., the same decrease in video quality as opposed to their maximum achievable qualities. Our simulations show that the proposed game-theoretic resource management provides a fairer and more efficient allocation of resources in terms of derived multimedia quality.

Multi-user multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider the Nash bargaining solution (NBS) for our resource management problem. We provide interpretations for the NBS for multi-user resource allocation: the NBS can be used to maximize the system quality. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation.