Federated learning (FL) gained importance in sensitive Internet of Things (IoT) environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, FL shifts data control away from the server, exposing it to non-independent and identically distributed (non-IID) problems caused by biased clients (IoT devices). This hinders the learning process by increasing execution time and cost. Current solutions alter the FL structure or compromise privacy by offloading clients’ raw data to an external server. To mitigate these limitations, this article proposes a solution to the non-IID problem by introducing an initialization phase, orchestrated by the server, that constructs high-quality initial models. These models can boost FL accuracy and convergence, regardless of whether IoT participants exhibit non-IID properties. Our proposed initialization scheme involves clients training over the same model sequentially, lessening the impact of aggregation, a primary cause of model degradation in federated approaches. Furthermore, a regulator algorithm deployed on the server maintains model integrity and mitigates catastrophic forgetting, enhanced by a client selection process that emphasizes the compatibility of IoT clients to cooperate effectively. Moreover, we devise an optimization scheme based on clustering and genetic algorithms to reduce the selection time while ensuring optimal performance in IoT networks. Experiments on MNIST, KDD, and CIFAR10 data sets show promising results in terms of initial model resiliency against catastrophic forgetting and non-IID settings. Additionally, our findings suggest that our approach can significantly enhance FL training in IoT applications by achieving 40% higher initialization accuracy and a 20% average improvement in end results compared to conventional methods, all while reducing computation time by 80% compared to similar approaches.

In this article, we tackle the network delays in the Internet of Things (IoT) for an enhanced Quality of Service (QoS) through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in QoS for IoT applications and may even disrupt time-critical functions. This article addresses the challenge of establishing fog federations, which are designed to enhance QoS. However, instabilities within these federations can lead to the withdrawal of providers, thereby diminishing federation profitability and expected QoS. Additionally, the techniques used to form federations could potentially pose data leakage risks to end-users whose data is involved in the process. In response, we propose a stable and comprehensive federated fog architecture that considers federated network profiling of the environment to enhance the QoS for IoT applications. This article introduces a decentralized evolutionary game theoretic algorithm built on the top of a genetic algorithm mechanism that addresses the fog federation formation issue. Furthermore, we present a decentralized federated learning algorithm that predicts the QoS between fog servers without the need to expose users’ location to external entities. Such a predictor module enhances the decision-making process when allocating resources during the federation formation phases without exposing the data privacy of the users/servers. Notably, our approach demonstrates superior stability and improved QoS when compared to other benchmark approaches.

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

Network delays cause a reduction in the Quality-of-Service (QoS) for Internet of Things (IoT) applications, and even render time-critical applications inoperative. The paper tackles the problem of forming fog federations that aim to improve the QoS. However, instabilities within fog federations might cause some providers to withdraw from the federation, and thus decrease the profit of the federations and the expected QoS. Moreover, federation formation techniques could potentially create privacy risks for end-users whose data is utilized in the process. This paper introduces a decentralized evolutionary game theoretic algorithm that tackles the problem of fog federation formation, as well as, providing a decentralized privacy-aware federated learning algorithm that predicts the QoS between fog servers for optimizing the formation procedure. The devised method provides better stability and increased QoS when compared to other benchmarks.

Federated fog computing is an answer for horizontally upscaling fog resources to improve the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic nature of some IoT’s crucial components, such as the ones of Internet of Vehicles (IoV), may hinder the QoS improvement and result in its deterioration instead. Specifically, delays can occur due to the unoptimized distribution of services and unbalanced network traffic loads on the fog nodes. The current federated fog architectures ignore the mobility of users during the formation of fog federations. In this work, we present an adaptive and efficient fog federation formation scheme using game theory according to the service requirements. The problem formulation in terms of forming the federations and offloading requests among fog members is formulated as an integer program, then modeled as a Hedonic game. We adopt the Merge & Split as a formation technique, where the federations that are not satisfied in terms of QoS merge with other federations that would enhance the service performance. Our adaptive fog federation formation mechanism is designed to cope with the environmental changes in the IoV paradigm. Experimental evaluation shows that our framework can acquire better QoS and lower time to form the federations compared to the literature.

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federations of fog providers for task offloading purposes. Nonetheless, a few challenges remain a burden for the formation of the federations. The formation mechanisms used to structure the federations of providers are still not fully stable. This causes a problem because a structureless federation can lead to an underperforming infrastructure. Furthermore, most of the literature ignored the honesty metrics of the providers and how trustworthy they are in allocating the agreed-upon resources for processing the tasks. Moreover, adopting a central reputation mechanism is questionable in terms of reliability due to many complications including the lack of consensus. In this work, we develop a Blockchain-based reputation mechanism for assisting the formation of fog federations for IoV applications. Our mechanism comprises on-chain smart contracts for storing and manipulating the providers’ reputations, and an off-chain Hedonic-based formation process that considers the parameters extracted from the chain to build the federations. We develop smart contracts using Solidity and deploy them on the Ethereum Blockchain. We test our mechanism using the EUA dataset as a proof of concept and compare it to other works in the literature. The results obtained show that our approach is able to enhance the overall payoff and quality of service in the IoV paradigm.

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

Federated learning using fog computing can suffer from the dynamic behavior of some of the participants in its training process, especially in Internet-of-Vehicles where vehicles are the targeted participants. For instance, the fog might not be able to cope with the vehicles’ demands in some areas due to resource shortages when the vehicles gather for events, or due to traffic congestion. Moreover, the vehicles are exposed to unintentionally leaving the fog coverage area which can result in the task being dropped as the communications between the server and the vehicles weaken. The aforementioned limitations can affect the federated learning model accuracy for critical applications, such as autonomous driving, where the model inference could influence road safety. Recent works in the literature have addressed some of these problems through active sampling techniques, however, they suffer from many complications in terms of stability, scalability, and efficiency of managing the available resources. To address these limitations, we propose a horizontal-based federated learning architecture, empowered by fog federations, devised for the mobile environment. In our architecture, fog computing providers form stable fog federations using a Hedonic game-theoretical model to expand their geographical footprints. Hence, providers belonging to the same federations can migrate services upon demand in order to cope with the federated learning requirements in an adaptive fashion. We conduct the experiments using a road traffic signs dataset modeled with intermodal traffic systems. The simulation results show that the proposed model can achieve better accuracy and quality of service than other models presented in the literature.

One of the promising features of 5G networks is device‐to‐device (D2D) communication that enables direct transmission between D2D user equipments (UEs). Besides the traditional cellular transmission mode, UEs can select between the reuse and dedicated modes. In this study the authors consider a scenario where a communicating D2D pair and a cellular UE that communicates with an evolved Node‐B can use the same spectrum. It is assumed that the cellular UE can move in the network while the D2D UEs are static. The movement of the cellular UE can affect the quality of the communication between the D2D pair. Therefore, the transmission mode between the D2D UEs might change to keep the best quality. In this study the authors propose a new mobility management and vertical handover algorithm that handles the transmission mode transition during the D2D connection to maximise the overall throughput. The algorithm uses distance from the border and critical direction set as mobility variables that are analytically determined. These variables are calculated using a mode selection map that is derived analytically when pathloss and fading models are used. Finally, in order to analyse the performance of the proposed handover algorithm, the authors analytically calculate handover rate and sojourn time metrics.

Over the past few years, many major wireless providers restricted their unlimited data plans and replaced them with limited-size fixed-price data packages. While this could be perceived as a disadvantage for customers, it helps the cellular wireless providers to reduce the traffic intensity at their base stations and this leads to a better service quality and higher rates for concurrently connected users. Hence, there is a tradeoff between the data volume and the data rates attributed to the users. To avoid the adverse effect of service inaccessibility, the cellular providers should carefully set the size and pricing of their data packages. Toward this end, the providers need a model that, together with proper market information, would allow to set the best prices for volume-based data and estimate the acceptable quantity of subscribers and their average data rate. In this paper, we propose such a model that quantifies the relationship between pricing and various market/system parameters such as data volume size, user budget, data rate, and service blocking probability. In particular, we formulate a set of revenue optimization problems for different spectrum assignment criteria like shared-carrier and dynamic sub-carrier allocation. Finally, several realistic scenarios are investigated in which the optimal network parameters are computed.

We investigate a cooperation scenario in which a cellular service provider (CSP) agrees to offer free Internet access to a certain number of applications offered by a governmental entity, referred to as a non-profit service provider (NSP). NSP also provides a WiFi Internet network via which its applications can be accessed free of charge but the network budget for the network expansion and making the access available to all potential users is limited. To overcome this issue, NSP aims to provide a part of its applications also available through a commercial cellular network. Thus, the main problem of NSP is finding the optimal portion of its budget that should be transferred to CSP as a side-payment for its service. To address this problem we propose a game that is modeling a multi-period contract between CSP and NSP. Since the coverage of NSP's free network affects the price of the CSP service, we solve a three stage Stackelberg game in which each network entity finds its best response. The outcome of our game is the optimally assigned NSP budget for the free cellular data access, the size of free cellular data volume, and the optimal service price of CSP in each period. We provide numerical examples in which we show the effect of the NSP budget and the number of subscribers on the optimal values.

Device-to-Device (D2D) transmission is a promising technology which enables the User Equipments (UE) in 5G cellular networks to directly communicate without going through the evolved Node-B (eNB). In such networks UEs should select a Transmission Mode (TM) amongst reuse, dedicated, and cellular modes by maximizing the overall transmission throughput or referring to a mode selection map. The selection map identifies the mode which results in the highest data rate given the current state of the network. This paper presents a new analytical framework for the derivation of the selection map of D2D enabled cellular networks. While this problem was treated in the literature through numerical simulations, our approach is analytical and consequently fast and precise.

Device-to-Device (D2D) communication is one of the new features introduced for Long Term Evolution-Advanced (LTE-A) and beyond networks. In this paper we address the problem of communication mode selection for a pair of User Equipments (UEs). In particular we assume that the paired UEs can choose from the reuse, dedicated, and cellular modes. While this problem was treated in the literature for Single-Input-Single-Output (SISO) case, we derive models for networks where UEs and evolved-Node-Bs (eNBs) exploit Multiple-Input-Multiple-Output (MIMO) techniques with four antennas at each side. This approach requires design of the pre-coding matrices at each node which poses a challenge in the reuse mode due to the non-convexity problem. To solve this issue we propose a distributed iterative algorithm. The numerical study compares the performance of the MIMO based approach with that of the SISO case, and the results show that the presented MIMO based mode selection method provides significantly higher network throughput than that of the SISO based mode selection method for the same D2D transmission range.

We consider wireless mesh networks with cognitive ability of the wireless routers' radios. The cognitive ability is a cost efficient manner to increase available bandwidth but requires an adaptive bandwidth management mechanism to deal with dynamics of primary users' activities. In this paper, we investigate the joint channel allocation and routing in cognitive wireless mesh networks including the channel reuse opportunities in order to improve the network performance. In particular we propose an economic framework for adaptation and control of the network resources with the goal of network profit maximization. The economic framework is based on the notion of state dependent node shadow price that is derived from Markov decision theory. The node shadow prices are used as routing metrics while their average values are used to allocate the channels among the different nodes. Simulation results illustrate the network profit maximization and effectiveness of the proposed channel allocation scheme that is integrated with a channel reuse algorithm.

A framework for decentralized control of fuzzy discrete-event systems (FDESs) has been recently presented to guarantee the achievement of a given specification under the joint control of all local fuzzy supervisors. As a continuation, this paper addresses the reliable decentralized control of FDESs in face of possible failures of some local fuzzy supervisors. Roughly speaking, for an FDES equipped with n local fuzzy supervisors, a decentralized supervisor is called k-reliable (1 ≤ k ≤ n) provided that the control performance will not be degraded even when n - k local fuzzy supervisors fail. A necessary and sufficient condition for the existence of k-reliable decentralized supervisors of FDESs is proposed by introducing the notions of M̃uc-controllability and k-reliable coobservability of fuzzy language. In particular, a polynomial-time algorithm to test the k-reliable coobservability is developed by a constructive methodology, which indicates that the existence of k-reliable decentralized supervisors of FDESs can be checked with a polynomial complexity.

In this paper, we consider the problem of resource allocation in two-tier networks taking into consideration nondense femtocell deployments. The following limitations can be remarked from the prior work in the field of resource allocation: (1) resources are underutilized due to the equal power distribution in macrocell, (2) access to public users in femtocells is restricted to avoid depriving own subscribers transmissions, and (3) degradation of signal-to-noise ratio due to noise effects has not been evaluated. To overcome these limitations, we propose a joint power and bandwidth allocation among two tiers together with base station selection using genetic algorithm. Our solution is able to: (1) maximize the overall system throughput, (2) find an appropriate serving base station for each user, and (3) bandwidth and power assigned to each user. Simulations were conducted and a comparison with a Weighted Water Filling algorithm is carried out.

Wireless mesh networks (WMN) are efficient and low cost solutions for the deployment of broadband access in various environments. To support real-time applications such as multimedia and emergency services, WMNs must provide quality of service (QoS) guarantees and QoS continuity protection. While the capacity of single radio WMNs may severely limit the QoS for such traffic, multiradio WMNs can overcome this restriction and provide additional links to support better QoS mechanisms. This paper describes two self-configuration mechanisms to establish mesh topologies in IEEE 802.11s multiradio WMNs: a distributed mechanism based on the portal announcement protocol specified in the IEEE 802.11s standard to determine the initial channel assignment of the mesh points forming a backhaul network and a centralized mechanism to optimize the topology initially created once the network is stable. We also study the performance of topology formation in IEEE 802.11s multiradio WMNs and identify the various factors affecting delays in the creation and modification of topologies.

We consider a time division multiple access (TDMA) based cognitive wireless mesh network (CWMN), where pairs of mesh nodes communicate with each other by sharing a spectrum that is licensed to a primary network with multiple primary users (PUs) who have exclusive right to occupy the spectrum. All the mesh nodes are equipped with multiple antennas capable of beamforming. For such a system, we investigate joint link scheduling, beamforming and power control with the objective of maximizing the sum-rate of CWMN under the minimum data rate requirement constraint of each mesh link (mesh node pair), total transmit power constraint of CWMN in a time slot, and maximum allowable interference constraint of each primary user (PU). The sum-rate maximization subject to the above mentioned constraints gives rise to a non-convex mixed integer nonlinear programming (MINLP) problem which is practically intractable. In order to find an efficient solution of the MINLP, we employ an extended duality based algorithm that uses multiple penalty multipliers to remove the duality gap of the non-convex MINLP problem. Simulation results show that the extended duality based solution performs very close to the optimal solution obtained by exhaustive search algorithm.

Service Overlay Networks (SON) can offer end to end Quality of Service by leasing bandwidth from Internet Autonomous Systems. To maximize profit, the SON can continually adapt its leased bandwidth to traffic demand dynamics based on online traffic trend estimation. In this paper, we propose novel approaches for online traffic trend estimation that fits the SON capacity adaptation. In the first approach, the smoothing parameter of the exponential smoothing (ES) model is adapted to traffic trend. Here, the trend is estimated using measured connection arrival rate autocorrelation or cumulative distribution functions. The second approach applies Kalman filter whose model is built from historical traffic data. In this case, availability of the estimation error distribution allows for better control of the network Grade of Service. Numerical study shows that the proposed autocorrelation based ES approach gives the best combined estimation response-stability performance when compared to known ES methods. The proposed Kalman filter based approach improves further the capacity adaptation performance by limiting the increase of connection blocking when traffic level is increasing.

In this paper, we are addressing the problem of autonomic resource management for Virtual Private Networks (VPNs). Resources management is one of the important problems facing most Internet Service Providers (ISPs). As a solution, the Autonomic Service Architecture (ASA) is proposed in the literature to automate the resources management. Although, this model is able to improve ISPs' performance by automatically adjusting the resources allocation of each customer, it still suffers from two main limitations. First, this model increases the ISPs' revenue in a non-optimal way. Second, this model has no mechanism to prevent customers' exaggeration that can lead to an inefficient resource utilization, and violate the contracted Service Level Agreements' (SLAs) terms. Customers might exaggerate by asking for more resources during and after the SLA negotiation session, especially in the case of multimedia streaming, where this can waste the available network resources. This is due to the fact that customers would like to guarantee their Quality of Services (QoS). To overcome the above limitations, we propose an Autonomic Resources Management Mechanism (ARMM) that increases the ISPs' revenue by allocating resources based on the auction mechanism, where resources are granted to the best bidders. Additionally, we propose a threat model based on Vickrey-Clarke-Groves (VCG) mechanism that is able to penalize exaggerated bidders according to the created inconvenience. Since in our framework, customers are assumed to be rational, they will avoid asking for more unneeded resources. Simulation results show that the ARMM model is able to efficiently utilize network resources, increase ISPs' profit, and customers' satisfaction rates.

Channel quality prediction is important for many networking mechanisms. This paper considers long-term prediction with the prediction distance of some hundred milliseconds. The prediction works in the context of Adaptive Coding and Modulation, which actually converts the task of continuous value prediction into discrete value prediction. In this work, pattern management is also considered as a key mechanism to reduce memory and processing overheads. Performance evaluation is carried out using measurement traces to evaluate the prediction algorithm with different parameter settings and matching measures.

The considered Service Overlay Networks (SON) lease bandwidth with Quality of Service (QoS) guarantees from a multitude of Internet Autonomous Systems, through service level agreements (SLA) with Internet Service Providers (ISP). This bandwidth is used to establish SON links and deliver end-to-end QoS for real time service connections. The leased bandwidth amount influences both the admitted traffic and network cost, affecting the network profit. This gives the network operator the opportunity to optimize the profit by adapting the network resources to changing traffic and SLA costs conditions. We propose a novel approach that maximizes the network profit based on traffic measurements and SLA cost changes. The approach uses an economic model that integrates the network routing policy with the adaptation of the SON link capacities. While performing the adaptation of leased bandwidth, the connection blocking constraints are also maintained. The proposed adaptive optimization approach is based on a reward maximizing routing policy derived from the Markov Decision Process theory although it can be applied to other routing policies. Analytical models as well as simulation of a measurement based implementation of the proposed models are used to evaluate the performance of the proposed approach.

We consider a spectrum sharing based cognitive radio network where a secondary multiple-input multiple-output (MIMO) communication link coexists with a primary user by opportunistically utilizing the transmit spectrum originally allocated to the primary user (PU). The secondary link employs orthogonal space-time block coding (OSTBC) at the transmitter and maximum likelihood (ML) decoding at the receiver. For this system, we focus on designing linear precoders assuming the knowledge of the transmit antenna fading correlations at the secondary user transmitter (ST) for both the links between PU receiver (PR) and ST (ST-PR), and SU receiver (SR) and ST (ST-SR). Given a limited power budget of ST, the precoder is designed to minimize the minimum-distance average pair-wise error probability (PEP) at SR subject to the average interference-power constraint at PR. The original optimization problem is non-convex and we propose to solve this problem using a nonlinear programming method called the augmented Lagrangian algorithm. Simulation results show that this scheme performs better than previously designed suboptimal schemes at all signal to noise ratio (SNR) levels.

Recent technological advances in code division multiple access (CDMA) with multiuser detection (MUD) allow to consider this technology for future wireless Ad Hoc networks. Due to the fundamentally different physical layer architecture, application of MUD in Ad Hoc networks requires novel approaches for medium access control (MAC) and scheduling mechanisms in order to take advantage of the new features. This paper proposes a new MAC and scheduling paradigm which addresses three design issues: distributed dynamic code assignment that avoids code collision, distributed scheduling scheme that provides fairness among contending nodes, and organization of the data transmission based on multiuser detection. Simulation model is used to verify the performance gains from the increased spectrum reuse due to multiuser reception and from the reduced interference due to multiuser detection. This is done by comparisons with existing MAC paradigms, such as CSMA/CA and parallel CDMA.

Multiuser detection based Medium Access Control (MAC) can give significant gains in throughput and Quality of Service (QoS) when applied to wireless Ad Hoc networks. To realize these gains, one has to implement a distributed neighborhood scheduling that provides the desired performance objectives. In this paper, we propose an approach for analyzing and comparing optimal or suboptimal distributed neighborhood scheduling schemes with different objectives. Then, we demonstrate the viability of this approach by implementing a scheduling scheme that uses Start Time Fair Queuing (STFQ) algorithm and by comparing its performance to a published suboptimal distributed scheduling for multiuser detection based MAC. In particular, the numerical results show that the delay performance of the priority voice packets can be significantly improved by using STFQ algorithm.

Variable environments in ad hoc networks require a joint control of physical (PHY) and medium access control (MAC) layers resources in order to optimize performance. In this paper, we propose a framework to perform such cross-layer control and optimization. The PHY layer and cross-layer engine estimate and predict the channel variations to evaluate the linkpsilas future packet error rate (PER), for MAC layer optimization. We consider high capacity code division multiple access (CDMA) ad hoc networks where nodes are equipped with matched filter receivers.

Recent technological developments in code division multiple access (CDMA) with multiuser detection (MUD) make multiple packets reception a more appropriate model for the physical layer of future wireless networks. To take advantage of the new features, a shift of responsibility from transmitters to

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

Capacity analysis in CDMA systems is quite difficult due to the system complexity and interdependence of performance of all connections. Analytical models usually suffer from many simplifying approximations and presumed uniformity of traffic conditions. On the other hand, event driven simulators are time consuming both in the software development and numerical study phases, especially when a multi-cell environment is considered. In this paper we describe a model which combines some advantages of the two approaches. In particular it can provide results in time comparable with analytical models while taking into account interdependencies between the connection parameters the same way as in the simulation model. The model can be compared to capacity evaluation at a given time instant in a simulation model but with a statistical representation of the mobiles. Hence the name statistical snap-shot. In the paper we analyze the main features of the model. Numerical results obtained from the analytic model are found to be within 10% of the results obtained from a detailed simulation study.

A unified framework for traffic control and bandwidth management in ATM networks is proposed. It bridges algorithms for real-time and data services. The central concept of this framework is adaptive connection admission. It employs an estimation of the aggregate equivalent bandwidth required by connections carried in each output port of the ATM switches. The estimation process takes into account both the traffic source declarations and the connection superposition process measurements in the switch output ports. This is done in an optimization framework based on a linear Kalman filter. To provide a required quality of service guarantee, bandwidth is reserved for possible estimation error. The algorithm is robust and copes very well with unpredicted changes in source parameters, thereby resulting in high bandwidth utilization while providing the required quality of service. The proposed approach can also take into account the influence of the source policing mechanism. The tradeoff between strict and relaxed source policing is discussed.

Code-division multiple-access (CDMA) is a serious candidate for personal communication systems at 1.9 GHz in North America. We consider the issue of bandwidth management in a CDMA integrated wireless-access network with heterogeneous services. A framework for adaptive connection admission in the up-link direction is proposed. It is based on estimation of the interference at the base station receivers. The estimation algorithm employs a linear Kalman filter which is driven by a measurement of the interference and by predicted traffic parameters of the admitted connections. We derived several generic variants of the control architecture for the up-link direction to assess the main characteristics of the framework and to determine the trade-offs between complexity and performance. They vary from a fixed strategy with fixed power control to an adaptive strategy with full information about network state and adaptive power control. A numerical study of the proposed framework shows that the estimated value of the average interference adapts well to the real value under nonstationary and nonuniform environment. This feature results in high network utilization for arbitrary traffic conditions.

A fundamental problem in connection oriented multiservice networks (ATM and STM) is finding the optimal policy for call acceptance. One seeks an admission control policy that efficiently utilizes network resources while at the same time being fair to the various call classes being supported. The theory of cooperative games provides a natural and precise framework for formulating such multicriterion problems as well as solution concepts. The authors describe how this framework can be used for analysis and synthesis of call admission strategies in broadband networks. In particular they investigate the Nash (1950), Raiffa-Kalai-Smorodinsky (Raiffa, 1953; Kalai and Smorodinsky, 1975), and modified Thomson (Cao, 1982) arbitration solutions from game theory. The performance of all solutions is evaluated by applying the value iteration algorithm from Markov decision theory. The approach is illustrated on a one-link network example for which the exact solutions can be achieved. The results indicate that the arbitration schemes from game theory provide some attractive features especially when compared to traditional control objectives: blocking equalization and traffic maximization. The authors also compare the optimal solutions with some simplified policies belonging to four different classes: complete sharing, coordinate convex, trunk reservation, and dynamic trunk reservation. The comparison indicates that in many cases, the trunk reservation and dynamic trunk reservation policies can provide fair, efficient solutions, close to the optimal ones.

A state-dependent policy for call admission and routing in a multi-service circuit-switched network is synthesized. To meet different requirements the objective function is defined as the mean value of reward from the network. Policy iteration is applied to find the optimal control. Assuming link independence the network reward process is decomposed into the set of link reward processes thereby significantly reducing complexity. The approach is implementable for large systems if certain approximations are used. A simulation study shows that the algorithm converges in two iterations, exhibits good traffic efficiency, and provides a flexible tool for performance allocation among services. The approach also constitutes a framework for studying, synthesizing and optimizing other call admission and routing strategies. In particular the results of sensitivity analysis are used to compare the proposed decomposition approach with that developed by F. P. Kelly (1988) for optimization of a load sharing policy in telephone networks.>

A model for node-to-node blocking probability evaluation in loss networks with state-dependent routing minimizing the reward from carried calls is synthesized. The model is based on the link independence assumption and results in an extended set of fixed point equations solved by repeated substitutions. The policy maximizing the reward from the network and the network performance under this policy are evaluated at the same time. The extensive numerical study covers the issues of accuracy, convergence, and control of performance allocation among different call classes.>

A state-dependent call admission and routing policy for a multiservice circuit-switched network is analyzed. The policy is based on decomposition of the Markov decision problem into a set of separable link problems. To provide an exact link analysis model a value iteration algorithm was offered. This allows examination of the accuracy of several approximations used to reduce the complexity of the problem. The numeral study showed that the convergence of the analyzed strategy is achieved in at most two iterations. The study also showed the good traffic efficiency of the approach and confirmed the predicted ability to control the distribution of call classes grade of service. The approach, together with its sensitivity analysis with respect to the arrival rates, provides a very general framework for studying, constructing, and optimizing other call admission and routing strategies. The results of sensitivity analysis are used to compare the proposed decomposition approach with the decomposition approach developed by F.P. Kelly (1988) for optimization of a load sharing policy. Also, the relationship to other routing strategies based on Markov decision theory is investigated.>

Admission control and routing in circuit-switched networks are investigated with two types of traffic: wideband with delayed call set up and narrowband operating in loss mode. Markov decision theory is used to construct a state-dependent routing policy. Optimality conditions for a load sharing routing are derived from the first-order Kuhn-Tucker equations. These models together with a direct routing scheme are used in a simulation study to determine the best approach for control of traffic with delayed call setup. State-dependent routing is found to be superior to direct routing and load sharing routing, although its operation is much more complex.>