The rapid development and scaling of mobile telecommunications networks, together with related domains such as the edge-cloud continuum have raised significant concerns regarding energy consumption and environmental sustainability. Addressing these concerns requires a focus on CPU energy consumption, as CPUs are among the largest energy consumers in these systems. This paper investigates existing techniques, with a focus on CPU idle states (C-states), performance states (P-states), and frequency scaling governors implemented at both hardware and software levels. These mechanisms enable the dynamic adjustment of CPU parameters, providing opportunities to optimize power consumption, frequency, voltage, and overall system performance. In this regard, three CPUs with different architectures from well-known manufacturers, Intel® and AMD®, are thoroughly examined. A comprehensive dataset, collected under three load scenarios (idle, medium, and high), is used to support the analysis, reflect realistic runtime conditions, and enable a comparison of the technological differences in how these parameters are exposed and utilized.

The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.

The exponential rise of intelligent Internet of Things (IoT) devices and the development of Cyber-Physical Systems (CPS) pose new challenges and requirements for modern applications. These include the need for seamless interconnectivity and interoperable interaction between various physical and virtual elements. The enrichment and transformation of IoT technologies to support such interactions is undergoing, considering the need for convergence with edge and cloud computing technologies and the management of IoT applications across resources in the computing continuum. This broader sense of connectivity is tightly connected with the development of Digital Twins (DT), which take advantage of the development of virtual counterparts of IoT devices and CPS. Novel architectural approaches are required to manage complex DTs’ topologies, collectively forming a Digital Twin Network (DTN) that acts as a middleware to provide advanced communication, efficient orchestration, and autonomous decision-making capabilities. This manuscript presents an architectural approach and a relevant open-source software stack implementation -called VOStack- for developing DTs. VOStack is open and modular by design, while it tackles IoT interoperability and convergence challenges with edge and cloud computing technologies. VOStack is thoroughly evaluated under various deployment schemas, virtualization techniques, and based on the provision of an IoT application in the context of a smart city scenario, demonstrating efficient utilization of resources and high efficiency of Machine Learning (ML)-driven orchestration mechanisms.

Question Answering (QA) over Knowledge Graphs (KGs) has greatly benefited from the rapid growth of Large Language Models (LLMs), which enable the translation of natural language questions into Cypher queries. Most existing approaches rely on one-shot generation via in-context learning or on fine-tuning LLMs; however, both strategies often struggle to generate accurate or executable queries, particularly when dealing with complex or unfamiliar graph schemas. To address these limitations, in this work we propose CyVerACT, an agentic workflow for Text-to-Cypher generation that empowers LLMs with execution- and schema-aware feedback mechanisms. CyVerACT leverages CyVer, a software tool that evaluates Cypher queries in terms of syntax validity and semantic compliance with respect to a specific KG schema, and detects their points of failure. The system customizes the input graph schema based on the input question and iteratively refines the generated queries taking advantage of the error metadata from CyVer to guide subsequent LLM generations. We evaluated and compared CyVerACT to existing single-shot generation and iterative refinement approaches in two publicly available Text-to-Cypher datasets of 2180 entries across various domains and complexities, using both foundational (e.g., GPT-4o, LLama-3) and fine-tuned state-of-the-art models. Experimental results demonstrate that the proposed workflow significantly improves query correctness and execution success rates, achieving up to 52.7% gain in accuracy in terms of syntax validity and schema access, and 13.5% gain in exact match.

The evolution toward sixth-generation (6G) networks necessitates integrated resource management solutions to address the interdependencies between network segments, such as Radio Access Network (RAN) and Edge Cloud (EC) infrastructures. Unified management of network and compute fabrics is crucial for achieving seamless service delivery, end-to-end power efficiency, and delay guarantees, while resiliency becomes a key enabler for adapting to various application demands and diverse network segment conditions. In this context, this paper proposes a unified framework for dependable wireless EC networks that jointly addresses the problems of RAN selection and Service Function Chain (SFC) embedding to minimize the total power consumption across network segments under end-to-end delay SFC deployment constraints. The framework iteratively solves these problems, considering the interdependencies between RAN ingress points and the EC network resource constraints. To deal with the high dimensionality of the considered parameters and achieve timely and scalable decision-making, a coalition formation game optimizes RAN selection, while a delay-aware heuristic approach undertakes the power-efficient embedding of multiple SFCs within the EC network. Simulation results demonstrate the framework’s efficiency in reducing power consumption compared to segment-specific approaches, highlighting the importance of cross-segment dependencies. Also, the adaptability of the proposed unified modeling and the framework’s scalability are demonstrated, ensuring resilient performance under varying network parameter settings.

Approximate and delayed computing have emerged as promising paradigms to offer flexibility in computational accuracy and strategically differentiate tasks between edge and cloud execution to enhance resource utilization. However, these approaches introduce tradeoffs, potentially compromising accuracy on one hand and increasing latency on the other. In this paper, we explore the integration of approximate and delayed computing paradigms within the edge-cloud computing continuum. Users can either offload tasks for approximate computing at the edge or opt for exact but potentially delayed computing at the cloud. In this context, the joint problem of computation task offloading and data compression is formulated and solved as a non-cooperative Game in Satisfaction Form. Each user autonomously determines the amount of task to offload for either computing option and the percentage of data compression for approximate computing, aiming to achieve an acceptable accuracy-latency tradeoff. The formulated game admits a Satisfaction Equilibrium (SE) point, which is concluded using a Reinforcement Learning (RL)-based algorithm. Simulation results demonstrate the performance of the proposed task offloading framework in the achieved accuracylatency tradeoff compared against different offloading strategies.

Collaborative Machine Learning (ML) paradigms, such as Federated Learning (FL), suffer from unequal client contributions and adversarial behavior, where clients deliberately degrade global model accuracy via outdated or poisoned updates. In this paper, we address fair client collaboration and adversarial behavior detection and mitigation using a combined reputation-aware incentive and robust aggregation approach. First, the long-term client reputation across FL epochs is estimated using a variant of the Shapley value, which offers polynomial complexity, contrariwise to the latter. Client reputation is then used to weight model updates during global model generation, effectively mitigating the impact of model poisoning and replay attacks. Moreover, it is used to determine informed monetary rewards for the clients on the server side, which, in turn, guide their efforts in the number of model iterations employed during local training. The interplay between server and clients in their reward and effort decisions is modeled as a Stackelberg game, which concludes fair client participation. Numerical results via modeling and simulation validate the effectiveness of both mechanisms against state-of-the-art and baseline alternatives.

In this paper, we address the challenge of performing effective joint wireless data-energy transfers in distributed mobile energy-harvesting networks. In principle, wireless power transfer resembles energy harvesting, however, it exhibits its own special features. We develop a holistic, backpressure-inspired technique, describing the evolution of each node’s queue-battery state, and we define an optimization problem with the objective of improving the balance of energy. We implement a dual Lagrange multipliers solution and determine the key variables influencing the system’s behavior. We investigate the overall energy transfer from the periphery to the core network in cases of traffic-stressed core nodes, and through analysis and simulation, we demonstrate the theoretical and practical potentials of this framework and its potential use for greener and self-sustainable networks.

Though extensive research efforts have been devoted to the problem of computing resource pricing, they mainly focus on single computing paradigms. In this paper, we provide a holistic approach to this problem, by treating the whole computing continuum, consisting of cloud, edge, and fog computing providers, simultaneously offering their resources to the users. Within such a complex setting, we establish the concept of symbiotic computing resource pricing and sharing, where the computing providers and the users coexist within a mutually beneficial ecosystem, sharing services and resources as a means of ensuring their business survival and service satisfaction. Under this prism, we introduce two key pricing families, namely the non-cooperative one which involves competition and is treated through game theoretic approaches, and the cooperative resource pricing (full or partial), which addresses complex scenarios through optimization and coalition. A thorough performance assessment is provided, through modeling and simulation, in order to highlight and quantify the key characteristics and tradeoffs of the various resource pricing approaches introduced.

The development of social and emotional competencies of students is associated with positive impact on their character development, progress on school activities, establishment of qualitative social relationships, overall well-being and health. Similar effects are also noticed at group level, since improvement of such competencies lead to more inclusive and collaborative climate within a classroom. By considering these positive impacts, in the current work we propose some inventories based on an existing open-access Emotional Intelligence (EI) model to make it applicable for the assessment of social and emotional competencies of students. The basis for our work regards the EmoSocio open-access EI model, while three adaptations are provided for different age groups (6–8, 9–12, 13–18 years old). The detailed versions of the EmoSocio inventories are evaluated and validated based on their wide usage within classrooms in a set of schools across Spain. Their usage is based on the application of a methodology for the assessment of social and emotional competencies of students that takes advantage of the adoption of information and communication technologies.

In the era of online learning, automated dynamic suggestions for fruitful collaborations among learners have become crucial. We propose an algorithm that takes into account ethical considerations and suggests interactions among students for collaborative involvement in group activities in an inherently fair manner: all students participate in fairly weighted activities and have a balanced number of weighted interactions. Students' preferences are taken into consideration and can be expressed either directly via their statements or indirectly via their conversations in the context of learning gamification. A synthetic preference graph that simulates students' views and thoughts is constructed. Moreover, we proceed to build a model for personalized interactions that prevents user isolation. The quality of the evolution of interactions is measured based on the diversity of interactions, the interaction graph edit similarity that affects storage cost, granularity of graph construction and fairness criteria. We use deep machine learning for sentiment detection in dialogues in order to conduct sentiment analysis of real data in the context of an online game chat among students. We categorize text as positive, negative or neutral in terms of emotion and produce student profiles. We produce embeddings with an accuracy of 92%. This analysis is extended to all texts in the discussion, resulting in a preference profile for each learner, from which ultimately a quantitative estimate of potential interaction derives for use in the interaction graph. Our approach can be utilised in the teaching practice as an efficient novel framework for online learning activities and gamification, and serves as an automated dynamic method for fair collaboration choices in teaching practice.

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms’ trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers’ monetary incentives, profit, and the platforms’ trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms’ monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms’ reputation. Simulation results demonstrate TOPMG’s operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies.

The efficient operation of the unified Integrated Sensing and Communication (ISAC) – Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices’ externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices’ externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia’s operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability.

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

In the emerging landscape of Integrated Sensing and Communication (ISAC) networks, achieving energy efficiency while concurrently performing sensing and communication tasks remains challenging. This paper introduces the GENESIS framework, a novel solution that empowers User Equipment (UEs) to make informed decisions regarding their transmission power allocation, optimizing the energy efficiency of sensing, communication, and data reporting to the gNB (gNodeB) functions. Initially, a novel ISAC network paradigm is proposed, where the gNB employs rewards, such as monetary incentives, to motivate UEs to engage in sensing, data collection, and reporting within its coverage area based on the principles of Contract Theory. The proposed GENESIS framework integrates the incentive mechanism with an optimal resource management technique which facilitates UEs to make energy-efficient decisions that balance their dual roles of sensing and communication, distributedly, while maximizing overall energy efficiency. The resulting multi-variable resource management problem is formulated as a non-cooperative game, establishing the existence and uniqueness of a Nash Equilibrium. Through modeling and simulation, we demonstrate GENESIS benefits, showcasing its energy-efficient operation and rapid convergence to optimal operational points.

Integrated Access and Backhaul (IAB) networking paradigm and the use of mm-wave technology have emerged as key enablers for the deployment of B5G/6G systems. In this paper we introduce the SynergyWave framework that empowers the IAB nodes and the users to independently optimize their transmission power levels, while simultaneously the IAB nodes perform optimal bandwidth splitting across the access and backhaul links. The key objective of SynergyWave framework is the enhancement of the energy efficiency of each participating entity in a decentralized and autonomous manner. Exploiting the channel modeling framework established by the 3rd Generation Partnership Project (3GPP) for mm-wave networks, we initially model the achievable data rate for both the access and backhaul links in the IAB network. Subsequently, a two-stage energy efficiency optimization problem is formulated and treated based on a Stackelberg game theoretic approach. In particular, it models and optimizes resource allocation in mm-wave IAB networks, determining optimal bandwidth splitting and uplink transmission power levels for IAB nodes and their users. The SynergyWave framework is assessed via modeling and simulation, and the obtained numerical results demonstrate that substantial energy efficiency improvements can be achieved for both users and IAB nodes.

The cloud-to-edge-to-IoT continuum represents a seamless flow of data processing and management, spanning from centralized cloud services to distributed edge computing and interconnected IoT devices. This paradigm can become very challenging in real implementations, especially in the presence of multiple stakeholders using proprietary and heterogeneous software and hardware. The Horizon Europe NEPHELE project proposes the virtualization of IoT devices through a specific software stack called Virtual Object Stack (VOStack) that promotes openness and interoperability. In this paper, we present an implementation of VOStack using W3C Web of Things (WoT) standard in a post-disaster domain for two different types of IoT devices: a ground robot (Turtlebot2) for navigation and mapping in an unknown environment, and a Raspberry Pi 3 acting as a wireless sensor network gateway. We propose an application graph for the resulting hyper-distributed application (HDA) and present our first implementation to validate the proposed solution.

Multi-provider multi-user multi-access edge computing provides a recent market-driven networking paradigm facilitating the user data offloading process. In this paper we introduce the AGORA framework, which employs a sophisticated multi-leader multi-follower Stackelberg game that jointly optimizes the data offloading, computing resource allocation, and computing resource pricing, all facilitated through a non-cooperative game-theoretic approach. In order to support the aforementioned modeling and approach, a novel utility function that quantifies the users satisfaction, factoring in the computing service cost, and an innovative profit function for the MEC providers is introduced, emphasizing the market penetration and the computing service provision costs. Numerical results, obtained via modeling and simulation, demonstrate AGORA’s remarkable adaptability, accommodating homogeneous and heterogeneous user computing demands, while simultaneously outperforming proportional fairness resource allocation approaches, and significantly enhancing the MEC providers’ profitability and the users’ satisfaction from the edge computing services.

In various IoT applications it is essential that the autonomous connected devices have the most up-to-date information, either to be read or posted. In this work, we consider a general setting of a wireless multihop network with multiple data flows and study the interactions of backpressure routing, congestion control, clean energy harvesting and Age-of-Information (AoI). We propose a heuristic scheme that is based on optimal source data rates and routing decisions, enhanced with heuristically determined AoI-based features in the form of a weight scaling method. The feasibility of the solution in terms of satisfying queue stability is proven, and the obtained upper bound on the queue lengths depends on the quotient of the max versus the min weight value. Numerical evaluations point out the improvements of the heuristic scheme in terms of delivering fresh information with priority, as well as the tradeoffs between AoI and optimality.

Green operation is of paramount importance in 6G and zero-energy wireless nodes can support it. To fully exploit the potentials of zero-energy wireless multi-hop networks, it is essential to jointly optimize their source data rates, routing and transmission power decisions, which is a significantly complex problem, in particular under the uncertainties introduced by the wireless channel states and the energy harvesting processes on the nodes. In this paper, we tackle the aforementioned problem under the assumption that wireless nodes operate only based on their batteries that charge solely via ambient energy harvesting. A plan for the network operation for a future time horizon is computed using scenario-based optimization techniques to account for stochasticities. The derived problem formulation is non-convex and is solved via a novel heuristic method that iteratively solves appropriately parameterized convex approximations of the original problem. At convergence, the obtained solution is feasible to the original non-convex problem. Numerical results illustrate the effectiveness of the proposed solution compared to the standard non-convex solver Ipopt and showcase the behavior of the network under heterogeneous scenarios.

Rate-Splitting Multiple Access (RSMA) has been recently recognized as a more general multiple access technique that overcomes the limiting factors of its predecessors related to the signal decoding complexity and interference management tradeoff. In this paper, we investigate the application of the RSMA technique to facilitate the users’ concurrent offloading to multiple servers in a multi-server Multi-Access Edge Computing (MEC) system. Each user fully offloads different parts of its computation task at the available MEC servers (or a combination of them) using the same frequency band. We aim to minimize the sum of users’ maximum experienced delay among the different MEC servers, stemming from both the offloading and processing, by jointly optimizing their computation task assignment ratios to the servers, their allocated common-message rates, common and private-message transmission powers, and computing resources related to each server. The formulated min-max-sum problem is non-convex, and its optimization variables are highly coupled. By examining its structure, we equivalently transform the problem and further decompose it into two independent sub-problems that separately provide solutions to the radio and computing resource allocation problems. Numerical results show the effectiveness of the proposed solution in terms of the users’ experienced delay and the proposed algorithm’s real execution time.

The positive impact of the development of social and emotional competencies of students is well documented, with benefits being evident in various aspects at individual and classroom level. Based on these findings, multiple social and emotional training programs have been created and combined with methodologies for their proper implementation within classrooms. However, the development of relevant tools to assess these competencies has not followed a similar pace. The few assessment techniques currently available require extensive expertise from teachers in the area of social and emotional training, making accurate and efficient assessments difficult to achieve. To address this issue and improve the assessment process, we introduce the EmoSociograms psychometric tool, an open-source software designed for easy application by teachers in both in-person and online classrooms. We detail the main components and functionalities of EmoSociograms, along with an evaluation based on its usage in primary and secondary education schools.

Crowdsourcing has been widely employed to collect information, either at regional or global scales, about different phenomena, by engaging user communities, in order to complement or even substitute other specialized and expensive means and sources of data. In such a setting, the design of crowdsourcing models that can jointly provide appropriate rewards to the users in order to incentivize them to participate in the crowdsourcing process, while at the same time provide the necessary information to potentially various tasks (mapped to different geographical areas) announced by a requester is of high research and practical importance. In this paper, a novel dynamic crowdmapping framework is introduced, to enable the users autonomously select the geographical area, and thus corresponding task, where they will contribute their available information based on a hedonic coalition formation game. Based on the proposed hedonic coalition formation game, the requester also allocates appropriate rewards to the users considering their quality and quantity of information. The existence of a Nash-stable and individual-stable coalition formation is proven and a hedonic coalition formation algorithm is introduced to determine the stable coalition formation. The performance evaluation of the proposed framework is achieved via modeling and simulation.

Community-driven energy initiatives have recently emerged as key enablers in the realization of efficient energy management systems, focusing in particular on trading and management of energy. In this article, we aim at introducing a novel community-based load balancing and prosumers incentivization framework in smart grid systems, based on the theory of hedonic community formation games. Such an approach enables the prosumers to autonomously select the most beneficial partition (community) they should join, in terms of optimizing their achieved payoff, while accounting for both the discounts offered by the provider and the load balancing characteristics of each community. Following such modeling, the existence of a Nash-stable and individually-stable partition is mathematically proven and a distributed hedonic community formation algorithm is designed, that converges to the stable solution. The performance of the proposed approach is achieved via modeling and simulation, and detailed numerical results demonstrate its operational characteristics and its benefits when compared to alternative strategies.

Information Centric Networking (ICN) paradigm exploits the in-network caching capacity to support the process of fast and efficient content distribution. In addition to the algorithmic and implementation challenges associated with the decision-making of content placement, the sustainability of content caching frameworks heavily depends on the design of appropriate network economics models to define and support the interactions among the involved players. In order to treat this need, in this paper, considering multiple Content Providers (CPs) while exploiting the in-network caching model, we particularly examine the joint problem of maximizing the CPs profit and their market penetration in terms of attracting a large portion of customers. The problem is formulated as a non-cooperative game among the CPs and the existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven. The performance evaluation of the proposed network economics-based approach is achieved via modeling and simulation, while its superiority against other alternatives is demonstrated.

Increasing the security and robustness of Positioning, Navigation, and Timing (PNT) systems is a critical issue towards exploiting the PNT service at its full capacity. In this paper, we treat the joint problem of designing a secure and robust alternative PNT solution that supports the targets' PNT services, while simultaneously detecting and ejecting malicious nodes from the system that aim at deteriorating the proposed PNT solution's accuracy. The overall problem is formulated as a non-cooperative game among the targets and other collaborator nodes in order to jointly minimize their personal experienced positioning and timing error, as well as the overall system's error. The theory of potential games is adopted to prove the existence of at least one Pure Nash Equilibrium (PNE), while a log-linear-based reinforcement learning (RL) algorithm is proposed to enable the targets and collaborators to determine such a PNE. A detailed analysis for attack detection is presented considering both single-attack and distributed denial of service (DDoS) attack scenarios, where the malicious collaborators can fake their coordinates and their transmission power, while an overall PNT methodology including their ejection from the system is outlined. The performance evaluation of the proposed approach is achieved via modeling and simulation.

Online Social Networks (OSNs) have become part of our everyday life, as a means of interacting with people, sharing content, attending events, etc. Influencers are professional OSN users, who have a large loyal audience and use the OSNs to market various goods or services based on brand partnerships. In this paper, we introduce a novel mechanism to enable OSN users to become influencers by strategically deciding their activity within the OSN. Initially, the concepts of social coordinates and social communities are proposed. Then, a non-cooperative game is introduced to enable the users to make optimal decisions regarding their activity in the OSN in order to become part of an influencers' social community and enjoy the benefit of additional followers. We show that the non-cooperative game is an exact potential game with at least one Nash Equilibrium and we introduce a distributed algorithm to determine its Nash Equilibrium. A detailed set of numerical results, based on real data extracted from Instagram, show the pure operation and performance of the proposed framework, as well as the impact of the social coordinates on the users' decisions. Also, a real-life case study is presented to show the applicability of the proposed framework in Instagram.

Integrated Sensing and Communication (ISAC) has recently emerged as an additional communication service within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) era, through which distributed nodes are able to communicate their sensing information to a Base Station (BS) using integrated signals. In this paper, we study the coexistence of ISAC with other communication types of the nodes, by introducing a software-defined framework to control the nodes' uplink transmission powers related to each service. Each node is simultaneously engaged in two types of communications with different BSs for ISAC and generic data transmission to the cloud via dual connectivity. The uplink power splitting/control problem between the BSs is formulated as a non-cooperative game in satisfaction form, through which each node autonomously concludes to a Satisfaction Equilibrium (SE) point that meets its minimum ISAC and pure communication-oriented requirements. Different achievable SE points are analyzed, while an Reinforcement Learning (RL) and a searching-based algorithm are introduced to conclude to the SE and Minimum Efficient SE (MESE) of the studied problem. Simulation results demonstrate the operation of the algorithms and the overall proposed framework in achieving an efficient share of the resources to the different services.

The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.

The Connected and Automated Mobility (CAM) services enabled by 5G connectivity are expected to change the automotive industry by offering safe, secure, and efficient autonomous capabilities to vehicles. These services depend on the ultra-fast/reliable connectivity offered by 5G, to ensure that all vehicles remain always connected and aware of their environment. This pre-condition however is challenged in cross-border areas, where vehicles change countries and consequently network service providers and may experience significant service degradation or even complete service unavailability for an extended period (up to minutes with legacy mobile networks). This paper presents a thorough evaluation of the effects of inter-PLMN Handovers on CAM applications’ performance including an analysis of the impact of different data roaming schemes, neighboring 5G networks’ interconnection (over public internet or via a direct leased line) and CAM application placement (remote Cloud vs nearby Edge). The analysis is based on real-life data from the neighboring 5G networks of the 5G-MOBIX project’s Greece-Turkey Cross-Border Corridor and offers insights on the expected mobility interruption time and end-to-end (E2E) latency of 5G enabled CAM applications when crossing national borders. The results indicate that with proper network configuration the 3GPP Rel. 15 NSA 5G networks can meet and even exceed the E2E latency requirements of certain CAM use cases, but mobility interruption time during HO remains a significant challenge.

The evolution of the Industrial Internet of Things (IIoT) and edge computing enables resource-constrained mobile robots to offload the computationally intensive localization algorithms. Naturally, utilizing the remote resources of an edge server to offload these tasks encounters the challenge of a joint co-design in communication, control, estimation, and computing infrastructure. We introduce a set-based estimation offloading framework, for the specific case of the navigation of a unicycle robot toward a target position. The robot is subject to modeling and measurement uncertainties, and the estimation set is calculated using overapproximation techniques that alleviate additional computations. A switching set-based control mechanism provides accurate navigation and triggers more precise estimation algorithms when needed. To guarantee the convergence of the system and optimize the utilization of remote resources, a utility-based offloading mechanism is designed, which takes into account both the dynamic network conditions and the available computing resources at the network edge. The performance of the proposed framework is demonstrated through simulations and comparison with alternative offloading schemes.

In this article, we address the problem of modeling and orchestrating the interactions between a museum and its visitors, viewing the system as a cyber–physical–social system (CPSS). In particular, the museum operator provides monetary rewards to the visitors in exchange for their contributions, which are expressed as their total number of provided feedback evaluations of visited exhibits over their touring time. The interactions among the museum operator and visitors are captured in appropriately designed utility functions following the principles of labor economics, while the visitors’ behavioral characteristics are utilized to define their unique types. Under such a setting and formulation, the goal of the museum operator is to optimize their profit and benefits while jointly satisfying the visitors’ quality of experience prerequisites as reflected via their utility functions. The corresponding optimization problem is treated and solved under the general and realistic cases of incomplete information, wherein the museum operator estimates the visitors’ types probabilistically. The resulting outcome, referred to as the “optimal contract,” jointly determines the visitors’ optimal contributions, as well as the museum operator’s optimal amount of personalized rewards provided to each visitor. The performance of the proposed approach is evaluated through modeling and simulation, and detailed numerical results are presented to demonstrate the key benefits of the proposed optimization approach versus either type-agnostic or heuristic alternatives.

In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user’s satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework’s consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users’ experienced performance compared to alternative approaches.

Maximizing the performance of modern applications requires timely resource management of the virtualized resources. However, proactively deploying resources for meeting specific application requirements subject to a dynamic workload profile of incoming requests is extremely challenging. To this end, the fundamental problems of task scheduling and resource autoscaling must be jointly addressed. This paper presents a scalable architecture compatible with the decentralized nature of Kubernetes [1], to solve both. Exploiting the stability guarantees of a novel AIMD-like task scheduling solution, we dynamically redirect the incoming requests towards the containerized application. To cope with dynamic workloads, a prediction mechanism allows us to estimate the number of incoming requests. Additionally, a Machine Learning-based (ML) Application Profiling Modeling is introduced to address the scaling, by co-designing the theoretically-computed service rates obtained from the AIMD algorithm with the current performance metrics. The proposed solution is compared with the state-of-the-art autoscaling techniques under a realistic dataset in a small edge infrastructure and the trade-off between resource utilization and QoS violations are analyzed. Our solution provides better resource utilization by reducing CPU cores by 8% with only an acceptable increase in QoS violations.

An effective way to accommodate the computing demands of Internet-of-Things (IoT) end-user devices without the intervention of a remote server, is to motivate the collaboration between them. The latter paradigm, termed as collaborative Mobile Edge Computing (MEC), allows an end-user device to act as service provider, by allocating excess computing resources for the computation of a service requester’s task, in exchange for adequate economic incentives. In this paper, we introduce a contract theory-based one-shot auction to model the computing resource trading between a service requester and the prospective service providers. Unlike existing works, we aim to account for the different types of asymmetric information arising during and after the contracting phase between the trading parties, regarding the service providers’ willingness to collaborate and their offered computing power. The service requester derives a set of optimal economic bids, having statistical knowledge of the providers’ private information, and each service provider autonomously selects the bid and its computing resource allocation that maximize its utility. The economic bid comprises a two-stage payment to secure the provider’s truthful collaboration both prior and after the contractual agreement. The effectiveness of the proposed model is validated by comparison against benchmark contract theory models that unilaterally account for the providers’ private information either prior/during or after the contracting phase.

In this paper, we consider a competitive aerial computation offloading environment, where two edge resource operators provide computing services on a time-slot basis to multiple users, via Unmanned Aerial Vehicles (UAVs), each bearing a mounted edge server. The aim of each UAV is to selfishly maximize the difference between its personal and the opponent UAV's utility, by competitively allocating its energy resources to the different users in the system. The problem is formulated as a Generalized Colonel Blotto (GCB) game, where the UAVs allocate their resources across a number of battlefields, i.e., the users, as competing players, seeking to win the battlefield by increasing the difference of their in-between allocated resources and thus, experienced utility. The overall framework is complemented by a Reinforcement Learning (RL)-empowered algorithm to account for the energy efficient scheduling of the UAVs' overall available energy in the different time slots, where the GCB game is realized. The performance evaluation of the proposed framework is achieved via modeling and simulation. The obtained numerical results demonstrate the operation of the proposed GCB game, under different levels of competitiveness between the UAVs, and assess the effectiveness and efficiency of the proposed RL algorithm against different comparative scenarios.

Autoscaling resources in a power-efficient way is essential to enable Green Computing resource management solutions. The development of dynamic resource provisioning techniques could lead to the minimization of power consumption and simultaneously guarantee high quality of service (QoS) inline with the workload demand. In this work, we introduce AHP4HPA, an autoscaling framework for Kubernetes Clusters, which is aligned with the Kubernetes architecture and state-of-the-art practices. We define resource profiles, namely a mapping between the QoS and the computing resources, to maximize the performance. Furthermore, Analytic Hierarchy Process (AHP) is exploited to dictate the scaling decision of the resources under various Key Performance Indicators (KPIs) toward power optimization of the allocated resources. To guarantee maximum performance of the deployed image classification application, an ARIMA model is dedicated to providing predictions regarding the incoming workload traffic. The framework is evaluated against a realistic dataset in a small-scale testbed. Numerical results indicate at least a 9% reduction of the average energy consumption when compared to other state of the art techniques.

In this paper, the problem of user recruitment by different competing marketing agencies (MAs) is treated, towards contributing to the development of an effective crowdsourcing framework applicable in Online Social Networks. Initially, a labor economics approach, following the principles of contract theory, is devised to enable the marketing agencies to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The overall objective of each MA in the aforementioned competitive environment is to maximize their personal benefit, i.e., total utility obtained by the user recruitment process, given its available total budget. The latter optimization problem is formulated and solved as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at properly incen-tivizing each user to report its information to this agency. A Pure Nash Equilibrium (PNE) is determined resulting in the optimal rewards that each marketing agency should provide to each user. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented to reveal the benefits of the proposed crowdsourcing model under different scenarios.

Capitalizing on the different available computing options across the network, the concept of computing continuum has recently emerged to efficiently manage the exaggerated computation demands of the numerous Internet-of-Things (IoT) users and applications. Nevertheless, the edge computing’s attractiveness to the users, in terms of its reduced incurred time and energy overhead, acts as an impediment in the realization of the envisioned computing continuum. In this paper, recognizing the potential of forwarding delay-tolerant tasks to upper computing layers, we design an incentivization-based mechanism for the offloading users, aiming to shift their preference from the edge to the upper fog computing layer. The corresponding mechanism comprises two stages, in which different models of Contract Theory are adopted. In the first stage, a users-to-edge server contract is formulated to determine the optimal amount of each user’s initially offloaded task at the edge that is allowed to be further forwarded to the fog, based on the user’s delay tolerance. Subsequently, an edge-to-fog server contract is formulated to account for the edge server’s tradeoff between the local execution and transmission overheads, deriving the most beneficial amount of the users’ tasks that ultimately reaches the fog. The overall mechanism is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

In this paper we exploit the advances provided by the Reconfigurable Intelligent Surfaces (RIS) technology, which allows for the software-defined control of the electromagnetic properties of the wireless medium, in order to introduce a low-cost and easily deployable ground-based alternative positioning, navigation, and timing (PNT) service. According to the proposed solution, the positioning of the target is performed by one original signal transmitted by one base station (BS), and three additional signals reflected by three RISs (selected from a set of available RISs), thus reducing significantly the required implementation and infrastructure cost. Different reinforcement learning algorithms, based on the gradient ascent and the log-linear learning models, are introduced and investigated to enable the target, to autonomously and dynamically select the optimal set of three RISs to be used for the optimization of its positioning accuracy. Subsequently, an iterative least square algorithm is realized to determine the position of the target. Detailed numerical results are presented that highlight the tradeoffs of the introduced reinforcement learning algorithms in terms of convergence and impact on achieved positioning precision, and demonstrate the superiority of the proposed methodology against existing ground-based PNT solutions.

The Minimum Collisions Assignment in an interdependent networked system is the problem of assigning a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. It has been shown in the literature that, when the number of resources is larger than the maximum degree of the underlying graph, there exists a randomized algorithm which converges, with high probability, to an assignment of resources having zero collisions. In this work we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We provide and analyze a distributed, randomized, algorithm that converges in a logarithmic number rounds to an assignment of resources over the network for which every node has at most a certain number of collisions.

Network slicing has been at the forefront of 5G network research, with various slicing orchestration architectures seeking to reap the benefits of slicing for the enhanced performance and reliability of 5G (and beyond) network services. In this context, cross-slice communication (CSC) has drawn significant attention, since CSC can foster interactions among services deployed in co-located slices, lowering the barrier for the consumption of services.To capitalize the benefits of CSC (e.g., reduced latency and cost), CSC should be established with the highest degree of co-location and also in a secure and policy-compliant manner. To this end, we present an orchestration framework that fulfills all main technical requirements for CSC instantiation. In this respect, we elaborate on the CSC instantiation workflows and shed light into the cross-layer interactions that span our proposed CSC orchestrator, the Network Function Virtualization Orchestrator (NFVO) and the Virtualized Infrastructure Manager (VIM). Our experimental results indicate that our proposed CSC orchestration framework introduces a negligible performance overhead and also incurs a minimal latency inflation compared to a direct form of inter-slice communication without any provision for security and resource isolation.

The emergence of Industrial Internet of Things (IIoT) requires the interconnection between robots, sensors, and the underlying network and computing infrastructure. Edge Robotics has emerged as a flexible paradigm that enables resource-constrained mobile robots to offload computationally intensive tasks of time/mission-critical applications. In this context, Edge Computing is essential for providing additional resources towards confronting the stringent performance specifications. This article presents the architectural concepts and capabilities of the NETMODE testbed, member of the Fed4FIRE+ federation, for the state-of-the-art experimentation with robotic applications. An evaluation of the proposed architecture is conducted using a SLAM algorithm which is a compute-intensive application.

The emerging technology of Wireless Power Trans-fer (WPT) has enabled mobile devices to replenish their batteries and increase their lifetime by exchanging energy with other devices in vicinity. In this paper, we study the problem of peer-to-peer WPT in a network of battery-constrained devices for which we aim to provide a fair energy allocation via mutual exchanges. In this respect, devices of lowest battery level remain functional for longer time, while respecting and satisfying a given set of wireless charging constraints. By taking into consideration the skewed energy availability in the network, as well as the loss induced by wireless energy transfer, we formulate and analyze three energy allocation schemes based on the concepts of lexicographic optimization and algorithmic graph theory, which, under different optimization criteria, aim to extend network lifetime. The performance of the proposed schemes is evaluated and compared in terms of energy efficiency and balancing quality through modeling and simulation over synthetic networks.

The Fifth Generation (5G) mobile communications together with software defined networking (SDN) and network function virtualization (NFV) are expected to enable a wide range of vertical use-cases. Different vertical industries with diverse service streams and sets of requirements should leverage the advanced capabilities of 5G networks through a single infrastructure to support the desired Quality of Service/Experience (QoS/QoE). In this paper, we focus on the Transport vertical and we study four novel service categories, each one consisting of one or more related scenarios, within the framework of the 5G Health, Aquaculture and Transport (5G-HEART) 5G PPP Phase 3 project. The first pass analysis of the envisioned vehicular services and their underlying operation, combined with the mapping of the mostly high-level functional user requirements to quantitative network Key Performance Indicators (KPIs) via a thorough and concise methodology, is essential for future testing with real pilots. Furthermore, our work paves the way towards efficient network slicing by exploring the interrelations between the identified KPIs and the respective target values that must be simultaneously satisfied over the same physical network infrastructure, in the context of the three 5G generic services.

The emergence of Unmanned Aerial Vehicles (UAVs) as part of the safety-critical and traffic alleviation infrastructure in 5G and beyond wireless networks, promotes the rethinking of the conventional resource orchestration management. In this paper, we propose a novel methodology that treats the uplink power allocation problem in UAV-assisted wireless networks, operated under Non-Orthogonal Multiple Access (NOMA), based on the principles of labor economics and Contract Theory (CT). The proposed approach specifically targets the challenge of imperfect Channel State Information (CSI) due to the uncertainties of the wireless links. The users are characterized by types that depend on their experienced channel conditions, which are typically unknown to the UAVs, while the latter probabilistically estimate the users’ types. The users’ transmission powers are iteratively optimized and determined, while an Reinforcement Learning (RL)-empowered user-to-UAV association procedure is realized. The overall framework is evaluated via modeling and simulation regarding its proper operation, effectiveness and efficiency, under different scenarios.

In this paper, we introduce a behavioral and labor economics based approach to address the challenge of citizens’ health data acquisition during a pandemic, in a smart city scenario consisting of the healthcare operator, multiple businesses, and citizens with wearable devices. Initially, a reinforcement learning approach is adopted in order for the citizens to select the business to visit, exploiting both social and physical characteristics of all involved entities. Subsequently, following the principles of behavioral economics, the problem of the citizens’ incentivization by the businesses to provide their health data via offering personalized rewards is studied. The solution of the corresponding optimization problem concludes to a contract between the business and each citizen associated with this business, containing the optimal reward and optimal portion of reported data. The process is completed by introducing an optimization framework, where the healthcare operator incentivizes the businesses to provide the collected health data to it, by providing them tailored rewards. This is founded on the principles of Contract Theory, where the healthcare operator aims at maximizing its benefit from the data acquisition process, while guaranteeing that the optimal determined contracts are acceptable by the respective businesses. Finally, through modeling and simulation, the performance, effectiveness, and robustness of the overall proposed framework is demonstrated, under various realistic scenarios.

Traffic matrices (TMs) contain information that is essential for network management, traffic engineering, and anomaly detection. However, constructing a TM through direct traffic measurements has a high administrative and computational cost. A more feasible approach is to estimate the TM from the easily obtainable link load measurements. In this paper, we address the issue of traffic matrix estimation (TME) from link loads using a deep generative model – namely, a variational autoencoder (VAE) – to solve the respective ill-posed inverse problem. In particular, we train the VAE with historical data (previously observed TMs) and we leverage the trained decoder to transform TME into a minimization problem in the latent space, which in turn can be solved by employing a gradient-based optimizer. Furthermore, the trained decoder can be used for traffic matrix synthesis, i.e., for generating synthetic TM examples that have “similar” properties to the samples of the training set. Finally, we explore the incremental optimization of the sequence of objectives constructed from the sequence of decoders that we obtain at different stages of the VAE training. The performance of the proposed methods is evaluated using a publicly available dataset of actual traffic matrices recorded in a real backbone network.

In this paper, we study the problem of caching at the network edge by taking into account the impact of recommendations on user content requests. We consider a heterogeneous caching network with small cells and mobile users who can offload traffic from the core network by delivering data via Device-to-Device (D2D) communication. Given the user mobility pattern, we derive for each user the expected waiting time to encounter cache-enabled devices and we propose two schemes in order to select the user equipment that will cache content and participate in the offloading. Expressing the user Quality of Experience (QoE) as a function of user-content relevance and its expected delivery delay, we formulate the problem of content placement and recommendations in caching networks as a user QoE maximization problem, which is known to be NP-hard. In order to address it, we provide two heuristic algorithms, the first focusing on user-content relevance and the second focusing on the content delivery delay. We evaluate the performance of our algorithms through simulation over synthetic datasets. The obtained results are compared with a state-of-the-art polynomial-time approximation algorithm and show that the proposed algorithms balance better the trade-off between solution quality and execution time.

To support the provisioning of modern services in a smart city environment, future communication networks need to be intelligently designed with respect to the city infrastructure and energy efficient utilization of resources. Unmanned Aerial Vehicles (UAVs) are already being utilized as part of a smart city wireless network infrastructure to provide on-demand connectivity and eliminate the network’s coverage holes, especially when communication conditions are unfavorable. Complementary to this, the adoption of Reconfigurable Intelligent Surface (RIS) technology allows for the creation of a more controllable smart wireless communications environment. In this article, a multi-user Non-Orthogonal Multiple Access (NOMA) communications system aided by a RIS and a UAV is studied. Based on a single-leader multiple-followers Stackelberg Game, we aim to jointly optimize the overall received signal strength at the UAV and maximize the users’ achieved energy efficiency. The UAV – acting as a leader - intelligently steers the RIS-reflected signals in order to enhance the corresponding received signal quality, by determining the RIS elements’ effective phase shifts. This, in turn, is exploited by the users (i.e., followers), which through the formulation of a non-cooperative game, where each user aims at maximizing its achieved energy efficiency, they determine their optimal uplink transmission power. The proposed optimization framework is evaluated via modeling and simulation, demonstrating the significant power savings and the ultimate users’ satisfaction occurring by the introduction of the RIS.

In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints.

In this article, we consider the problem of recommendations to the users of an online social network (OSN), through an information diffusion aware recommender system (IDARS). We map the assignment of recommendations in influence networks to a problem of selecting anl-cover of the minimum total cost, which is defined to be a set of assignments such that each user in the OSN is recommended of at leastldifferent items at the minimum defined cost. This corresponds to a special case of the minimum weighted partition set cover problem, which is a generalization of the minimum weighted set cover problem, both of which are proven to be NP-hard. We formulate a corresponding integer programming problem and we apply a linear programming (LP)-based branch and bound (BnB) methodology for its solution. We also propose a greedy algorithm, denoted as CoveR, which we show to be an O((Δ/δ)·H(Δ))-approximation for thel-coverage problem, where Δ and δ are the maximum and minimum degree of an influence network, respectively, and H(Δ) is the Δ th harmonic number. We investigate CoveR's performance through extensive simulations on both synthetic and real networks, which indicate that the quality of its solution is comparable to the one obtained by the BnB method, while at the same time outperforms other information diffusion-aware recommendation heuristics.

In this paper, to exploit the challenges and potential offered by the simultaneous use of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) transmission options in future 5G wireless systems, we aim at the proper modeling and transformation of the uplink power allocation problem. In particular, in this setting, each user has two degrees of freedom in the decision making process, namely its overall transmission power level, and the corresponding power investment to the OFDMA and/or NOMA based transmissions. The resulting multi-variable power allocation problem is treated and solved under three different perspectives, namely: 1) Games in Normal Form and Nash Equilibrium (NE); 2) Optimization techniques targeting system social welfare through a centralized optimal solution; and 3) Games in Satisfaction Form and Efficient Satisfaction Equilibrium (ESE). Based on these approaches, different solutions and stable operation points are identified and their properties are analyzed. An in depth evaluation and comparison of the various obtained outcomes is achieved, via modeling and simulations. The focus is placed on the impact and the interplay of the NOMA specific features, including the potential over-exploitation of the available bandwidth, the fairness in accessing it, and the interference treatment. It is also shown that, using the satisfaction form games for the users to converge to the ESE, provides an efficient and promising user-centric modeling approach to the power allocation problem, as the system adapts to the users’ application needs, while at the same time eliminates a significant amount of interference.

Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate IoT applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes' communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation.

Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings.

The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven by exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to an NE, and their tradeoffs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios.

Given the substantial penetration of social networks in citizens' everyday life activities, the success of a public safety system depends on the citizens' incentivization by the Emergency Control Center (ECC), and their effective effort contribution in the overall disaster management operation. In this paper, we introduce a formal method based on the principles of Contract Theory, to identify the optimal rewards to the citizens from the ECC's perspective, and the optimal invested effort from the citizens' side, referred to as contract pairs. The identification of these contract pairs (i.e., rewards and respective efforts) between the ECC and each citizen, depend on each citizen's social and communication characteristics that are used to define their specific type and profile, while they are properly reflected in the corresponding designed utility functions to be optimized. The problem under consideration is treated for both cases of complete (ideal) and incomplete (realistic) information availability, with respect to the level of knowledge of the ECC about the exact type of each citizen. The overall framework was evaluated via modeling and simulation, in terms of its efficiency and effectiveness, by studying multiple operation approaches and scenarios.

In this paper, we address the network tomography problems of inferring the multicast routing tree topology and estimating core link performance characteristics (i.e., loss rate, jitter) based on end-to-end measurements from a source node to a set of destination nodes. We extend the agglomerative hierarchical clustering algorithm that works in a bottom-up manner and iteratively joins siblings (i.e., nodes with the same parent) by incorporating the concepts of reciprocal nearest neighbors and nearest neighbors chains. We employ two alternative ways for calculating the required distance matrix of terminal nodes. One based on additive tree metrics and another utilizing several normalized dissimilarity measures on the binary sequences of received/lost probes maintained at each node. Finally, we evaluate the performance of the proposed algorithm in terms of estimation accuracy and correctness of the inferred logical routing tree over real network topologies constructed in an open testbed of the Fed4FIREP1us federation.

In the era of 5G networks, the demand for high quality service provisioning is growing extremely fast. The enabling of Network Function Virtualization and Network Slicing in the scope of 5G network aims to meet the strict requirements of various business cases. Alongside, the complexity of deployment such services becomes also higher, regarding the differences between infrastructure capabilities and the plethora of various individual requirements. This fact makes the selection of the appropriate infrastructure for slice deployment a complex, but also, a major process, as the optimization of the selection leads to the satisfaction of the user and the better resource allocation from the provider's perspective. In this work, an Edge PoP Selection framework for network slice deployment is proposed. This framework takes into account the user's hard and soft requirements and performs a two-stage selection. The selection of the appropriate infrastructure is based on a multi-criteria decision making method. The proposed framework is evaluated and compared with simple filtering and single-objective selection approaches. The promising results show the importance of the two stage framework in order to simultaneously meet the user's requirements and the optimal utilization of the resources.

The increasing resource demand and diversity of network services is taken under serious consideration by the various stakeholders, driving the architecture design of 5G (and beyond) networks. Network slicing, as a prominent aspect of next-generation network architectures, aims at satisfying the diverse service requirements in terms of throughput, latency, reliability, and/or security. However, the prevailing way of slice provisioning, i.e., in the form of isolated bundles of computing, storage, and network resources, makes cross-slice communication inefficient, especially at the network edge. This inevitably hinders opportunities for Business-to-Business (B2B) synergies at the event of service co-location. In this paper, we study this novel aspect of network slicing, i.e., cross-slice communication (CSC). We particularly promote a form of optimized CSC, at which two co-located slices can establish peering in a secure and controlled manner, by confining peering traffic within the boundaries of the datacenter, while still preserving the important aspect of resource isolation. Such optimized CSC can foster synergies between service providers without additional latency or traffic in the backhaul/transport network. In this context, we investigate various ways to establish optimized CSC at edge computing infrastructures, based on functionalities offered by state-of-the-art management and orchestration (MANO) frameworks, such as OpenSourceMANO.

The evolution of Internet of Things (IoT) has sparked significant research interest in edge computing. Within this scope and given the ever-increasing number of IoT and mobile devices, computation offloading is emerging as a cutting-edge and significant research area with enormous potential and practical applications.In this respect, we present the architecture design and experimental evaluation of an orchestration framework for smart computation offloading from IoT or mobile devices to edge cloud servers. The proposed orchestration platform, namely COSMOS, includes control-plane components for workload prediction, load balancing, and admission control. COSMOS is particularly tailored to the needs of an object identification service that receives images from a multitude of Points of Interest (PoIs), performs object identification using a trained model (based on Tensorflow), calculates the prediction accuracy, and finally returns to the end-users the identification outcome and accuracy along with useful information about the identified object. COSMOS has been deployed and evaluated in a large-scale experimental facility that employs OpenStack and OpenSourceMANO (OSM) for Network Function Virtualization (NFV) orchestration. Our experimental results indicate the feasibility of computation offloading for this object identification service and further uncover useful insights in terms of performance and scalability.

In this article, a sociotechnical consideration of the congestion management problem in museums is presented, by treating museums as dynamic social systems, where the momentum of the experience is controlled by visitors themselves. Visitors are considered as prospect-theoretic utility maximizers, whose behavioral risk attitudes affect not only their personal decisions and experiences but also those of others, thus creating an interdependent social system. To address the congestion problem within such a probabilistic and uncertain environment, pricing is introduced as an effective mechanism to drive visitor actions in efficient operation points, preserving museum operation stability. The corresponding problem of determining the time invested by each visitor at museum exhibits toward optimizing their obtained experience, as expressed via properly designed prospect-theoretic utility functions with pricing, is formulated and treated as a noncooperative game. The theory of S-modular games is adopted to prove existence and convergence to Nash equilibrium. Based on this framework, we study and analyze the validity and effectiveness of pricing as a tool to manage congestion in museums.

Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers' resources introduces high computation and communication uncertainty, in this paper we consider users' risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users' data offloading decisions under realistic conditions and behaviors are presented.

In this paper, a flexible resource sharing paradigm is introduced, to enable the allocation of users' computing tasks in a social cloud computing system offering both Virtual Machines (VMs) and Serverless Computing (SC) functions. VMs are treated as a safe computing resource, while SC due to the uncertainty introduced by its shared nature, is treated as a common pool resource, being susceptible to potential over-exploitation. These computing options are differentiated based on the potential satisfaction perceived by the user, as well as their corresponding pricing, while taking into account the social interactions among the users. Considering the inherent uncertainty of the considered computing environment, Prospect Theory and the theory of the Tragedy of the Commons are adopted to properly reflect the users' behavioral characteristics, i.e., gain-seeking or loss-averse behavior, as well as to formulate appropriate prospect- theoretic utility functions, embodying the social- aware and risk-aware user's perceived satisfaction. A distributed maximization problem of each user's expected prospect-theoretic utility is formulated as a non-cooperative game among the users and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal computing jobs offloading to the VMs and the SC, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation.

This paper introduces a Multi-Agency DisAster Management (MADAM) framework for Unmanned Aerial Vehicle (UAV)-assisted public safety systems, based on the principles of game theory and reinforcement learning. Initially, the information quality and criticality (IQC) provided by each agency to an UAV-assisted public safety network is introduced and quantified, and the concept of Value of Information (VoI) that measures each agency's positive contribution to the overall disaster management process is defined. Based on these, a holistic cost function is adopted by each agency, reflecting its relative abstention from the information provisioning process. Each agency aims at minimizing its personal cost function in order to better contribute to the disaster management. This optimization problem is formulated as a non- cooperative game among the agencies and it is proven to be an exact potential game, thus guaranteeing the existence of at least one Pure Nash Equilibrium (PNE). We propose a binary log- linear reinforcement learning algorithm that converges to the optimal PNE. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority compared to other approaches is demonstrated.

In this paper a novel dynamic spectrum management scheme for 5G Non Orthogonal Multiple Access (NOMA) wireless networks is proposed, where users are offered the option to transmit via licensed and unlicensed bands. Users are enabled to determine the optimal allocation of their transmission power in each one of the bands, while the unlicensed band is treated as a Common Pool Resource (CPR) - being non-excludable and rivalrous - which may collapse due to over-exploitation. Towards providing a pragmatic modeling approach for decision making under a realistic setting of probabilistic uncertainty, while properly capturing user risk perceptions, we model the corresponding optimization problem under the principles of Prospect Theory, removing the common assumption that subjects are behaving as neutral utility maximizers, a concept that does not reflect user risk behavior peculiarities. The corresponding problem is formulated as a CPR game, while the existence and uniqueness of its Pure Nash Equilibrium point are proven, and a user centric distributed algorithm is devised that obtains the corresponding solution. Detailed evaluation results are presented, highlighting the operation and superiority of the proposed framework against conventional Expected Utility Theory based approaches, while providing useful insights about user optimal decisions under realistic behaviors.

In this paper, we focus on the development and realistic evaluation of a resource allocation approach for cognitive radios implemented with Software Defined Radio (SDR) technology over two testbeds of the ORCA federation. Our cross-layer approach is based on a Markov Random Field (MRF) framework realizing a distributed computation among the secondary nodes of cognitive radios. This is the first implementation and real experimental evaluation of such a mechanism. New SDR functions for implementing various cognitive radio functionalities, such as spectrum sensing, distributed node synchronization, etc., were developed in GNU Radio from scratch. We demonstrated the feasibility of the MRF-based resource allocation approach and quantified various performance metrics of interest, such as allocation fairness and collision percentage. Through this development, several design principles of broader interest for SDR emerged (e.g., for spectrum sensing and collision detection design), while salient features of our framework requiring further research and development were discovered (e.g., need for parallel implementation).

The evolution of communication systems in the direction of heterogeneity and mass connectivity through the deployment of 5G compatible technologies, is posing significant challenges to Wireless Internet Service Providers (WISPs)towards enhancing spectral efficiency and ensuring steady and uninterrupted operation. In this paper, we introduce a holistic framework that dynamically combines multiple access technologies while accounting for the users' Quality of Service (QoS)prerequisites and risk preferences. In emerging future wireless networks with flexible access technology interfaces, both bands operating over Orthogonal Frequency Division Multiple Access (OFDMA)and Non-Orthogonal Multiple Access (NOMA)become simultaneously available to the users as potential options of communications and usage. OFDMA technology, due to organizing the available spectrum into distinct resource blocks, provides free of interference but of relatively limited bandwidth service to the users, whereas NOMA has the potential to provide superior spectral capacity by accommodating all users in a single carrier. However, the latter comes at the expense of resource fragility and potential failure from over-exploitation, due to its fully shared nature and excessive competition among users. Considering users' diverse behavioral patterns when probabilistic uncertainty of the shared system's resources is assumed, we model the resource control problem under the principles of Prospect Theory, and solve it as a Fragile Common Pool of Resources (CPR)game converging to a unique Pure Nash Equilibrium (PNE)point. Decentralization of users' decisions under the proposed pragmatic approach enhances the stability and network's performance, which are confirmed by a series of comprehensive numerical results.

Quality of experience (QoE), quantified via appropriately defined utility functions, has been widely used as a means to express user satisfaction in social systems. In such systems, subjects are usually assumed to be neutral utility maximizers, a concept that does not properly reflect the user risk-seeking behavior peculiarities. In this paper, we address the issue of incorporating and assessing the impact of visitor behavioral factors within the cultural heritage space, by exploiting the power of prospect theory. Exhibits of the cultural heritage site are organized in two main categories, namely, safe exhibits and common pool of resources (CPR) exhibits, based on their popularity and attractiveness. The latter ones are considered as nonexcludable and rivalrous resources in nature. Consequently, the obtained visitor QoE expressed via the prospect-theoretic utility function, heavily depends on the cumulative time spent by all visitors in these exhibits, thus making their behaviors and decisions interrelated, acting more like a social competitive environment. To determine visitor optimal time investment in different types of exhibits, while taking into account the potential interdependence of visitors decisions, a noncooperative game among the visitors is formulated and solved in a distributed manner, such that each visitor maximizes his own prospect-theoretic utility function. Detailed evaluation numerical results are presented, highlighting the operation and superiority of the proposed framework while providing useful insights about visitor decisions under realistic conditions and behaviors.

Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such as application average response time, and the optimal utilization of the computational resources of edge servers.

Multipurpose devices have emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able of interchanging their operation between different sensing modes providing a large mixture of information towards various IoT applications. In this paper, a novel framework is introduced to govern and properly define the dynamic operation of a multipurpose device network deployment. Initially, the problem of socio- physical energy-efficient device sensing mode selection is confronted. Each multipurpose device acts as a learning automaton and through a machine learning mechanism selects the most appropriate operation mode, in terms of maximizing the revenue/cost relation of the provider. In addition, towards improving the communication efficiency, a coalition formation mechanism among the nodes is proposed, which considers: (a) nodes' spatial proximity reflecting physical conditions such as channel quality, (b) energy availability, and (c) operation mode correlation between multipurpose devices expressing social metrics. Given the mode selection and coalition formation among nodes, a distributed utility-based power control mechanism is proposed to determine each device's optimal transmission power in a Non- Orthogonal Multiple Access (NOMA) wireless network environment in order to fulfill its Quality of Service (QoS) prerequisites. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.

In this paper we propose a topology control based approach for addressing the coverage problem in a planar region containing convex opaque obstacles. Such environments represent typical cases of realistic wireless sensor networks and Internet-of-Things deployments. Assuming the devices have the capability to modify their sensing ranges, our goal is to maximize the area covered by randomly dispersed sensors, while reducing their sensing energy consumption as much as possible despite the presence of convex obstacles. To address the former, we introduce a relevant framework capitalizing on the notion of the visibility polygon and propose two algorithms, a centralized (and a randomized version thereof) and a distributed one, which aim to maximize the ratio of covered area to consumed energy, while ensuring a minimum coverage percentage. Through analysis and simulation we demonstrate that the proposed schemes achieve energy efficient coverage, outperforming the plain assignment of maximum sensing range across the network.

Unmanned Aerial Vehicles (UAV) facilitate the development of Internet of Things (IoT) ecosystems for smart city and smart environment applications. This paper proposes the adoption of Edge and Fog computing principles to the UAV based forest fire detection application domain through a hierarchical architecture. This three-layer ecosystem combines the powerful resources of cloud computing, the rich resources of fog computing and the sensing capabilities of the UAVs. These layers efficiently cooperate to address the key challenges imposed by the early forest fire detection use case. Initial experimental evaluations measuring crucial performance metrics indicate that critical resources, such as CPU/RAM, battery life and network resources, can be efficiently managed and dynamically allocated by the proposed approach.

In this paper an analytical framework for the joint allocation of multiple physical resources under a dynamic personalized pricing setting, in a NOMA wireless network, is designed. Under our proposed user-centric paradigm, price is treated as a resource itself rather than simply being a control parameter and represents the user willingness to pay towards obtaining certain QoS levels. Each user expresses her satisfaction with respect to her and other users choices through a specifically designed utility function based on her unique characteristics and preferences. The resource allocation problem under consideration, becomes a distributed utility maximization problem, where each user updates her controllable parameters in an autonomous manner targeting at her satisfaction maximization. The problem is modeled and solved as a multivariable non cooperative game, admitting a unique Nash Equilibrium (NE), whose convergence is reached via a distributed and low complexity algorithm. Detailed numerical results, clearly demonstrate that the proposed framework allows the users to better exploit the system's resources, and therefore improve their overall satisfaction, while achieving significant improvements in the system operation in terms of power savings and achievable data rate.

Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type.

In this paper, the problem of joint users' uplink transmission power and rate allocation in NOMA wireless networks is studied, under the scenario that each user is simultaneously requesting multiple services. Each user is associated with a two-variable utility function that represents his satisfaction from his allocated resources (i.e., power and rate). In order to appropriately reflect the combined needs of the user for bundling different types of services under the same common umbrella, user's utility function consists of two parts while different percentages are adopted by each user for each part in order to express his need for real and non-real time services. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of S-modular multivariable games is adopted towards determining the Nash equilibrium point of the game. A distributed, iterative and low complexity algorithm for computing game's Nash equilibrium is introduced, while updating user's uplink transmission power and rate at the same step. Detailed numerical results exhibit the ability of the proposed framework to simultaneously satisfy diverse multiple services requested by the same user.

This paper tackles the problem of joint users' uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. Each user-either macrocell (MUE) or femtocell user equipment (FUE)-is associated with a two-variable utility function that represents his perceived satisfaction with respect to his allocated resources (i.e., power and rate). User's utility function is differentiated based both on the tier that the user belongs to and the service he requests. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of supermodular games is utilized towards treating the two-variable optimization problem and the inherent multidimensional competition that arises among the users. The existence of proposed game's Nash Equilibrium (NE) point is analytically shown, while game's convergence to its NE point is proven. A distributed and iterative algorithm for computing the desired NE is introduced, where the optimal values of each user's uplink transmission power and data rate are simultaneously updated at the same step. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated.

In this paper, we formulate a network utility maximization (NUM) problem, targeting an optimal cross-layer network operation, while considering time-varying and random possibly non-stationary wireless channels. As indicated in the literature, this problem imposes scalability constraints when the time horizon of the network control increases, impeding an online (i.e., real-time) application of its solution during the network operation. To achieve an online network control, in this paper, we leverage on model predictive control (MPC) or receding horizon control (RHC) for the solution of the NUM problem. Furthermore, MPC/RHC allows for the adaptation of the optimal controls in dynamic and evolving network conditions, in our case with respect to the wireless channels, the modeling parameters of which are estimated in an online fashion. We present and analyze the NUM problem, while we appropriately reformulate it for applying MPC/RHC. Then, we describe the MPC/RHC-based algorithmic solution, which determines the decisions for the online network control including power control, scheduling, routing, and congestion control, while we discuss stability and optimality issues. Finally, we evaluate the proposed methodology via numerical results and we show that the performance lies very close to the optimal one even for relatively small receding horizon lengths that significantly reduce the computational time complexity.

In this paper, we study, analyze, and evaluate a performance-aware cross-layer design approach for wireless multihop networks. Through network utility maximization (NUM) and weighted network graph modeling, a cross-layer algorithm for performing jointly routing, scheduling, and congestion control is introduced. The performance awareness is achieved by both the appropriate definition of the link weights for the corresponding application's requirements and the introduction of a weighted backpressure (BP) routing/scheduling. Contrary to the conventional BP, the proposed algorithm scales the congestion gradients with the appropriately defined per-pair (link, destination) weights. We analytically prove the queue stability achieved by the proposed cross-layer scheme, while its convergence to a close neighborhood of the optimal source rates' values is proven via an ε-subgradient approach. The issue of the weights' assignment based on various quality-of-service (QoS) metrics is also investigated. Through modeling and simulation, we demonstrate the performance improvements that can be achieved by the proposed approach-when compared against existing methodologies in the literature-for two different examples with diverse application requirements, emphasizing respectively on delay and trustworthiness.

Network Utility Maximization (NUM) is often applied for the cross-layer design and optimization of wireless networks. In most approaches, the NUM framework is based on the assumption of known or ideal wireless channel conditions. However, realistic wireless channel capacities are stochastic (time-varying and random) bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. In this paper, we apply the NUM framework to perform congestion and power control in wireless multihop networks while taking into account the stochastic Long Term Fading (LTF) wireless channels. Specifically, the wireless channel power loss is modeled via the use of Stochastic Differential Equations (SDEs) alleviating several assumptions that exist in state of the art channel modeling within the NUM framework such as the finite number of channel states or the stationarity. Based on that, we initially propose an algorithm for performing congestion control under stochastic LTF wireless channels. Next, the proposed algorithm is enhanced via power control aiming to further increase users' optimal utility by exploiting the random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results are presented to evaluate the performance and operation of the proposed approach.

Network utility maximization is often applied for the cross-layer design of wireless networks considering known wireless channels. However, realistic wireless channel capacities are stochastic bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. Based on NUM theory we develop a framework for scheduling, routing, congestion control and power control in wireless multihop networks that considers stochastic long or short term fading wireless channels. Specifically, the wireless channel is modeled via stochastic differential equations alleviating several assumptions that exist in state-of-the-art channel modeling within the NUM framework such as the finite number of states or the stationarity. Our consideration of wireless channel modeling leads to a NUM problem formulation that accommodates non-convex and time-varying utilities. We consider both cases of non orthogonal and orthogonal access of users to the medium. In the first case, scheduling is performed via power control, while the latter separates scheduling and power control and the role of power control is to further increase users' optimal utility by exploiting random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results evaluate the performance and operation of the proposed approach and study the impact of several involved parameters on convergence.

In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow.

Cloud computing has emerged as the computing paradigm that enables the delivery of utility-based IT services to users. The hyper-growth of Cloud computing has led to increased power consumption with significant consequences both in terms of environmental and operational costs. Hence, over the last years, attention has been drawn to optimizing energy consumption at the data center, aimed at the reduction of carbon footprints. However the world of Cloud Computing is constantly developing, with new concepts introduced while additional challenges arise. In this paper, a method for energy efficient resource allocation is proposed, in the context of a networked cloud environment. The method employs dynamic server consolidation by periodic VM migration. The approach is validated conducting performance evaluation via simulation, while it is compared against energy aware / non energy aware methods, using a set of both power indication and resource allocation metrics.

Achieving high Quality of Service (QoS) over wireless multihop networks calls for enhanced routing/scheduling algorithms. Towards this direction it has been shown in the literature that the Greedy Backpressure algorithm which combines routing based on greedy hyperbolic embedding with backpressure scheduling, achieves to improve delay while remains throughput optimal. However, the performance of such an approach is significantly affected by the selection of the corresponding spanning tree used for greedily embedding the network into the hyperbolic space. Our work aims exactly at addressing this issue, that is the construction of an appropriate spanning tree that improves the cost of the paths used by the Greedy Backpressure approach, when considering a more generic weighted network graph modeling. The latter allows us to take into consideration the link costs in the routing process, which in turn may result in the simultaneous improvement of multiple performance metrics. To address the problem under consideration, we propose a coalition formation game framework among the network nodes, so that they can decide cooperatively for the spanning tree, via trading their value functions designed to depend on the link weights. We prove that the stable outcome of the coalitional game is a spanning tree of the network, and study through simulations the induced improvement in the network performance. Furthermore, we extend the framework for a scenario with multiple costs on each link through multi-tree hyperbolic embedding.

Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization platform augmented with network and computing facilities.

Cloud-oriented content delivery networks (CCDNs) constitute a promising alternative to traditional content delivery networks. Exploiting the advantages and principles of the cloud, such as the pay as you go business model and geographical dispersion of resources, CCDN can provide a viable and cost-effective solution for realizing content delivery networks and services. In this paper, a hierarchical framework is proposed and evaluated toward an efficient and scalable solution of content distribution over a multiprovider networked cloud environment, where inter and intra cloud communication resources are simultaneously considered along with traditional cloud computing resources. To efficiently deal with the CCDN deployment problem in this emerging and challenging computing paradigm, the problem is decomposed to graph partitioning and replica placement problems while appropriate cost models are introduced/adapted. Novel approaches on the replica placement problem within the cloud are proposed while the limitations of the physical substrate are taken into consideration. The performance of the proposed hierarchical CCDN framework is assessed via modeling and simulation, while appropriate metrics are defined/adopted associated with and reflecting the interests of the different identified involved key players.

The cloud represents a computing paradigm where shared configurable resources are provided as a service over the Internet. Adding intra- or intercloud communication resources to the resource mix leads to a networked cloud computing environment. Following the cloud infrastructure as a Service paradigm and in order to create a flexible management framework, it is of paramount importance to address efficiently the resource mapping problem within this context. To deal with the inherent complexity and scalability issue of the resource mapping problem across different administrative domains, in this paper a hierarchical framework is described. First, a novel request partitioning approach based on Iterated Local Search is introduced that facilitates the cost-efficient and online splitting of user requests among eligible cloud service providers (CPs) within a networked cloud environment. Following and capitalizing on the outcome of the request partitioning phase, the embedding phase-where the actual mapping of requested virtual to physical resources is performed can be realized through the use of a distributed intracloud resource mapping approach that allows for efficient and balanced allocation of cloud resources. Finally, a thorough evaluation of the proposed overall framework on a simulated networked cloud environment is provided and critically compared against an exact request partitioning solution as well as another common intradomain virtual resource embedding solution.

Future communications consist of an increasing number of wireless parts, while simultaneously need to support the widespread multimedia applications imposed by social networks. These human-machine systems, driven by both real time social interactions and the challenges of the wireless networks' design, call for efficient and easy to implement, distributed cross-layer algorithms for their operation. Performance metrics such as throughput, delay, trust, energy consumption, need to be improved and optimized aiming at high quality communications. We investigate the coveted throughput-delay trade-off in static wireless multihop networks based on a "computer-aided" design of the backpressure scheduling/routing algorithm for networks embedded in hyperbolic space. Both routing and scheduling exploit the hyperbolic distances to orient the packets to the destination and prioritize the transmissions correspondingly. The proposed design provides us with the freedom of controlling its theoretical throughput optimality and of counterbalancing its practical performance through simulations, leading to significant improvements of the throughput-delay trade-off.

Joint power and rate control is a key element to the efficient use of wireless system resources especially when considering heterogeneous services with various transmission rates and requirements. Although several game-theoretic approaches have appeared to solve this problem their stable outcome is extracted independently or semi-jointly, and the corresponding equilibrium solutions introduce several inefficiencies. To eliminate some of them and achieve a more socially desirable energy efficient operational point, in this paper we combine pricing mechanisms with a joint utility-based uplink transmission power and rate allocation non-cooperative game formulation. The existence of a unique Nash equilibrium is shown, and the respective game's convergence is proven. A distributed, iterative and low-complexity algorithm for computing the desired equilibrium point is presented while the performance effectiveness of the proposed approach is evaluated via modeling and simulation considering both linear and nonlinear pricing.

Contemporary traffic demands call for efficient infrastructures capable of sustaining increasing volumes of social communications. In this work, we focus on improving the properties of wireless multihop networks with social features through network evolution. Specifically, we introduce a framework, based on inverse Topology Control (iTC), for distributively modifying the transmission radius of selected nodes, according to social paradigms. Distributed iTC mechanisms are proposed for exploiting evolutionary network churn in the form of edge/node modifications, without significantly impacting available resources. We employ continuum theory for analytically describing the proposed top-down approach of infusing social features in physical topologies. Through simulations, we demonstrate how these mechanisms achieve their goal of reducing the average path length, so as to make a wireless multihop network scale like a social one, while retaining its original multihop character. We study the impact of the proposed topology modifications on the operation and performance of the network with respect to the average throughput, delay, and energy consumption of the induced network.

In this paper, the problem of efficient distributed power control via convex pricing of users' transmission power in the uplink of CDMA wireless networks supporting multiple services is addressed. Each user is associated with a nested utility function, which appropriately represents his degree of satisfaction in relation to the expected trade-off between his QoS-aware actual uplink throughput performance and the corresponding power consumption. Initially, a Multiservice Uplink Power Control game (MSUPC) is formulated, where each user aims selfishly at maximizing his utility-based performance under the imposed physical limitations and its unique Nash equilibrium point is determined. Then the inefficiency of MSUPC game's Nash equilibrium is proven and a usage-based convex pricing policy of the transmission power is introduced, which offers a more effective approach compared to the linear pricing schemes that have been adopted in the literature. Consequently, a Multiservice Uplink Power Control game with Convex Pricing (MSUPC-CP) is formulated and its unique Pareto optimal Nash equilibrium is determined. A distributed iterative algorithm for computing MSUPC-CP game's equilibrium is proposed, while the overall approach's efficiency is illustrated via modeling and simulation. © 2006 IEEE.

Secondary ad hoc users of Cognitive Radio networks experience constant fluctuations of their link quality due to dynamic traffic behavior of their primary network counterparts. Several cognitive-aware MAC and routing protocols have been proposed in order to counterfeit properly such spectrum variations. In this paper, we introduce a vertical time-based control mechanism in the traditional protocol stack, aiming at properly balancing the inherent trade-off between instant local adaptations at MAC layer, against slower, but more globally aware rerouting reaction mechanisms. Based on localized and independent node decisions, an optimal feedback policy is proposed in order to exploit the most appropriate protocol layer in each case and provide per link channel assignments, such that flow rate requirements are satisfied with the least cost. We use Markov theory to examine the coexistence of primary and secondary networks, while the optimal decision policy is derived via a Markov Decision Process (MDP) and linear programming. Analysis and simulations are used for performance evaluation and together they confirm that the proposed time-based cross-layer feedback strategy contributes to higher network performance regarding flow rate requirements, sensing and rerouting costs.

Cognitive Radio (CR) techniques have been developed to allow ad hoc users to communicate with each other by exploiting the licensed bands of primary systems without disturbing the entrenched users. In this work, we take current approaches one step ahead and combine Topology Control (TC) techniques with CR technology, in order to improve operation and performance, even under the stringent and non-uniform arrangements of CR networks (CRNs). We propose a novel node-degree based Topology Control approach, denoted by Enhanced Cognitive Nearest Random Neighbor (e-CNRN), for multi-channel CRNs, aiming at maintaining network connectivity and adapting to environmental changes such as primary user activity and channel conditions. Compared with TC protocols in conventional ad hoc networks, e-CNRN requires only minimal local information and is specially designed to perform under non-uniform node arrangements, rendering e-CNRN a generic and robust distributed TC approach, especially suitable for multichannel CRNs as well. In addition, we leverage e-CNRN for distributively establishing a virtual common control channel for multi-channel CRNs. Through analysis and simulations we validate that e-CNRN guarantees network connectivity, while achieving efficient power control.

In this paper the problem of enhancing trust establishment in multi-hop wireless networks is addressed. Exploiting small-world features and based on preferential attachment and initial node trust values, we design inverse Topology Control methods that achieve to reduce the mean hop-distance between two nodes and increase the average trust value of the shortest paths. Based on continuum theory a mathematical framework is developed for the overall socially-motivated trust-based network churn mechanism. Analytical and simulation results exhibit the effectiveness of the proposed approaches for enhancing physical topologies, increasing the average trust path values, and thus further securing future communications systems.

Abstract Sampling has become an essential component of scalable Internet traffic monitoring and anomaly detection. This paper emphasizes on the analysis and evaluation of the impact of two‐stage sampling (TSS) techniques on network anomaly detection. Through the positive exploitation of the fact that sampled traffic is an incomplete and simultaneously biased approximation of the underlying traffic trace, we propose and analyze an enhanced two‐stage selective sampling approach, where an intelligent flow‐based sampling method that focuses on the selection of small flows that are usually the source of malicious traffic, is adopted. The performance evaluation of the impact of TSS on the anomaly detection process is achieved through the use and application of an entropy‐based anomaly detection method on a packet trace with data that has been collected from a real operational university campus network. The corresponding results demonstrate that the proposed approach improves and favors anomaly detection effectiveness, while at the same time reduces the number of sampled data, and in most cases achieves to even outperform the corresponding results of the unsampled case. Copyright © 2010 John Wiley & Sons, Ltd.

The transmission of confidential information over satellite links operating at frequencies above 10 GHz is studied in this paper. The major factor impairing the link performance at these frequencies is rain attenuation, a physical phenomenon exhibiting both spatial and temporal variation. Based on an accurate channel modeling, analytical expressions of the probability of nonzero secrecy capacity and the outage probability for this type of networks are provided, giving an information-theoretic approach of the problem of secure transmission for satellite networks. The analysis is extended in the case of two legitimate users and two eavesdroppers where the diversity gain increases or decreases, respectively, the probability of secure transmissions. Useful conclusions are drawn concerning the impact of various factors, such as frequency of operation, separation angles and climatic conditions on the aforementioned metrics through extended numerical results.

In this paper a novel framework for extending QoS to QoE in wireless networks is introduced. Instead of viewing QoE as an off-line apriori mapping between users' subjective perspective of their service quality and specific networking metrics, we treat QoE provisioning as a dynamic process that enables users to express their preference with respect to the instantaneous experience of their service performance, at the network's resource management mechanism. Specifically, we exploit network utility maximization (NUM) theory to efficiently correlate QoE and user-application interactions with the QoS-aware resource allocation process, through the dynamic adaptation of users' service-aware utility functions. The realization of the proposed approach in a CDMA cellular network supporting multimedia services is demonstrated and the achieved benefits from both end-users' and operators' point of view are discussed and evaluated.

Today's self-managing systems would ideally be able to adapt themselves (their internal structure or behavior), as well as to autonomously participate in larger, self-organizing systems. Analogously, the enterprises or other socio-economic systems autonomously manage themselves - they make decisions on how to adapt their structure and behavior, and how to organize with other entities in the environment. To connect internal self-adaptive with external self-organizational behavior, an enterprise is “aware” of itself and of its environment, and acts according to this awareness. This position paper proposes to address the challenges of a complex distributed self-managing system by making entities in such a system able to adapt themselves similarly to how companies manage themselves in socio-economic systems. To enable the knowledge transfer between these two fields, the paper proposes to utilize symbolic models which will be used by self-managing systems for knowledge representation and reasoning. This will make such systems in a way also self-aware and enable both self-adaptive and self-organizing capabilities. The paper discusses research directions to make this approach possible.

In this paper the problem of proficient users' network assignment in integrated CDMA/WLAN systems is addressed and a decentralized utility-based Network Selection algorithm aiming at optimizing user's and system's performance while satisfying multiple services' diverse Quality of Service (QoS) requirements is proposed and analyzed. The proposed approach favors the efficient provisioning of both real-time and non-real-time services, by exploiting the benefits emerging from the use of a common utility based optimization framework that provides the flexibility of uniquely modeling heterogeneous QoS prerequisites. Finally, via modeling and simulation it is demonstrated that significant performance improvements concerning both services' QoS requirements fulfillment and system's welfare are achieved through the proposed approach.

In this paper we address the problem of efficient power allocation in the uplink of CDMA wireless networks supporting simultaneously both real-time and non-real-time services with various and often diverse Quality of Service (QoS) prerequisites. Each mobile is associated with a nested QoS-aware utility function that characterizes its degree of satisfaction for the received service in terms of achieved goodput and corresponding power consumption. The problem is formulated as a non-convex non-cooperative Multi-Service Uplink Power Control (MSUPC) game where users aim selfishly at maximizing their utility-based performance under the imposed physical limitations. We first prove the existence and uniqueness of a Nash equilibrium point of MSUPC game, and then a distributed iterative algorithm is proposed in order to obtain MSUPC game's equilibrium point. Finally, the efficacy of the proposed approach with respect to the fulfillment of both types of services' QoS requirements is extensively illustrated via modeling and simulation.

In this paper we present a novel autonomic architecture for CDMA wireless networks, aiming at maximizing overall network's resources utilization while fulfilling various services QoS prerequisites. Founded on a common utility-based framework that provides enhanced flexibility in supporting various and often diverse QoS prerequisites, proper non-convex resource allocation optimization problems concerning both the downlink and uplink of the CDMA system, are set and solved. Then, decentralized algorithms for obtaining system's optimal resource allocation are proposed and used to enable user's QoS-aware selfoptimization functionalities via devising proper control loops that reside at mobile nodes and the base station. Finally, the efficacy of the proposed architecture is illustrated via modeling and simulation.

Abstract In this paper an enhanced anomaly detection approach based on the fusion of data gathered from various monitors spread throughout a wide area network is introduced. The proposed approach is based on the application of principal component analysis on multi‐metric‐multi‐link data, and provides an efficient and unified way of taking into account the combined effect of the correlated observed data, for anomaly detection purposes. It actually introduces a generalized anomaly detection methodology, capable of detecting not only volume based anomalies but also a much wider range of classes of anomalies, such as the ones that may result in alterations in traffic composition or traffic paths. The performance of the proposed multi‐metric‐multi‐link anomaly detection approach is evaluated via simulation, and is compared against the corresponding techniques that are based on the single‐metric analysis. Finally, its operational effectiveness is demonstrated in a realistic environment using real data collected from the core routers of the Greek research and technology network (GRNET). Copyright © 2008 John Wiley & Sons, Ltd.

Sampling has become an essential component of scalable Internet traffic monitoring and anomaly detection. A new flow-based sampling technique that focuses on the selection of small flows, which are usually the source of malicious traffic, is introduced and analysed. The proposed approach provides a flexible framework for preferential flow sampling that can effectively balance the tradeoff between the volume of the processed information and the anomaly detection accuracy. The performance evaluation of the impact of selective flow-based sampling on the anomaly detection process is achieved through the adoption and application of a sequential non-parametric change-point anomaly detection method on realistic data that have been collected from a real operational university campus network. The corresponding numerical results demonstrate that the proposed approach achieves to improve anomaly detection effectiveness and at the same time reduces the number of selected flows.

Sampling has become an essential component of scalable Internet traffic monitoring and anomaly detection. In this paper, the emphasis is placed on the evaluation of the impact of using intelligent flow sampling techniques on the anomaly detection process. Based on the observation that small flows are usually the source of many network attacks (DDoS, portscans, worm propagation) we first introduce a new flow sampling methodology that focuses on the selection of small flows and achieves to improve anomaly detection effectiveness, while at the same time reduces the number of selected flows. The performance evaluation of the impact of intelligent flow-based sampling on the anomaly detection process is achieved through the adoption and application of a sequential non-parametric Change-Point Detection anomaly detection method on realistic data that have been collected from a real operational university campus network.

In this paper we study the propagation of malicious software in wireless ad hoc networks under a probabilistic framework. We design topology control algorithms for the development of effective attack strategies by a malicious mobile node, based on the risk function metric, which indicates the network's vulnerability. Our approach takes on the attacker's perspective, in order to investigate the extent of its attack potentials, which in turn could be used for the effective design of network countermeasures. Our performance evaluation results demonstrate that the proposed risk-based topology control algorithms and respective attack strategies effectively balance the tradeoffs between the potential network damage and the attacker's lifetime, and as a result significantly outperform any other flat and threshold-based approaches.

In this paper we study the problem of providing short- term access time fairness in wireless communication systems with multimedia services, while still maintaining high system throughput by exploiting the advantages of opportunistic scheduling. Our approach aims at providing elastic short-term fairness to the users by setting upper bounds on the probability of receiving an amount of service smaller than their QoS requirements. This is achieved by introducing the information of users' received service within short-term intervals into the scheduling policy. By using this information our proposed algorithm reduces the channel's condition control on the selection of the next user to receive service, especially for users that do not satisfy their short-term requirements. Moreover, by setting different values for the users' observation service intervals and by defining appropriate service deviation functions for different classes of users, we provide enhanced flexibility in the control of fairness, while still exploiting channel adaptive scheduling to achieve high throughput.

In this paper opportunistic scheduling to achieve high throughput, while at the same time satisfy both long-term and short-term users' QoS constraints in wireless networks supporting multimedia services is adopted. This is achieved via the transformation of the corresponding QoS constraints into appropriate probabilities of accessing the systems' recourses. The users' scheduling procedure considering their corresponding access probabilities is obtained through a new proposed scheduling policy which allocates them fixed weights, initially computed, under the assumption of Rayleigh channels. The derived users' weights ratios force the opportunistic scheduling policy to meet the users' desired access probabilities and thus satisfy their respective QoS requirements.

In this paper we study the spreading of malicious software over ad hoc networks, where legitimate nodes are prone to propagate the infections they receive from an attacker or their already infected neighbors. Considering the susceptible-infected-susceptible (SIS) node infection paradigm we propose a probabilistic model, based on the theory of closed queueing networks, that aims at describing the aggregated behavior of the system when attacked by a malicious node. Due to its nature the model is able to deal more effectively with the stochastic behavior of the attacker and the inherent probabilistic nature of the wireless environment. The proposed model is able to describe accurately the asymptotic behavior of malware-propagative ad hoc networking environments, where the number of nodes is large. Using the Norton equivalent of the closed queueing network, we obtain analytical results for its steady state behavior, which in turn can be used to identify the critical parameters affecting the operation of the network.

In this paper a comprehensive and efficient incident detection approach is proposed, that uses probabilistic network and processing methodologies to exploit spatial and temporal correlations and dependencies in vehicular networks. The proposed approach, based on principal component analysis, provides an integrated way of effectively processing and organizing accumulated spatiotemporal information from a variety of different locations, vehicles and sources, and therefore allows for the development of enhanced and efficient transportation systems. The performance and operational effectiveness of our proposed incident detection methodology is achieved via modelling and simulation under various scenarios.

In this paper the problem of resource allocation for the downlink in code division multiple access wireless networks supporting multimedia services is addressed. A utility based power and rate allocation algorithm which aims at optimizing system's performance while satisfying multiple services' diverse quality of service (QoS) requirements is introduced and analyzed. For the efficient support of real-time users our proposed methodology takes into account their short-term throughput QoS constraints satisfaction, which is achieved by dynamically adapting their utility functions with respect to their corresponding short-term service performance information. Through modelling and simulation it is demonstrated that significant performance improvements are achieved in terms of the short-term throughput requirement satisfaction even for large number of real-time users, without any considerable loss in the total system's throughput.

The work presented in this paper1 focuses on a new approach in provisioning Quality of Service (QoS) in ad hoc wireless networks, aiming at making the best use of ad hoc networking as a candidate technology for next generation wireless networks. Specifically, a cross-layer QoS provisioning architecture for wireless ad hoc networks is introduced and described, based on the integration of the recently proposed service vector concept at the network layer and a delay bounded power efficient scheduling at the data link layer. It is demonstrated through modeling and simulations that this novel architecture can provide considerable performance improvements in terms of both power savings and enhanced QoS granularity in wireless ad hoc networks. Furthermore, the performance of the proposed scheme under various traffic arrival rates and distributions is evaluated.

The spread of active attacks has become a frequent cause of vast systems breakdown in modern communication networks. In this paper, we first present a probabilistic modeling framework for the propagation of an energy-constrained mobile threat in a wireless ad hoc network. The introduced formulation is used to identify and evaluate different attack strategies and approaches, which in turn can help in the development of efficient countermeasures for such attacks. Through modeling and simulation, we evaluate the impact of various parameters associated with the operational characteristics of the mobile attack node - such as transmission radius, mobility, energy - on an outbreak spreading and the evolution of the network. Furthermore, we introduce a new metric which indicates the overall infection-capability of each attack strategy and characterize their ability to harm the network according to this metric

In this paper an efficient Quality of Service (QoS)- constrained data aggregation and processing approach for distributed wireless sensor networks is introduced and evaluated. The objective of the proposed approach is to aggregate data on the fly at intermediate sensor nodes in order to improve the operational efficiency and effectiveness of the sensor networks, while at the same time still satisfying the latency and measurement quality constraints. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as the energy efficiency and end-to-end latency, are also evaluated and discussed.

In this paper, an efficient quality of service (QoS)-constrained data aggregation and processing approach for distributed wireless sensor networks is investigated and analyzed. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion, based on the availability of local only information. Data aggregation is performed on the fly at intermediate sensor nodes, while at the same time the end-to-end latency constraints are satisfied. Furthermore, a localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy, and buffer overflow, for given QoS requirements. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as energy efficiency, network lifetime, end-to-end latency, and data loss are also evaluated and discussed

Abstract Emerging real‐time communications and multimedia applications necessitate the provisioning of Quality of Service (QoS) in Internet. Recently, a new concept, referred to asservice vector, has been introduced to enhance the end‐to‐end QoS granularity, and at the same time, maintain the simplicity and scalability feature of the current differentiated services (DiffServ) networks. This work extends this concept to wireless ad hoc networks and proposes a cross‐layer architecture based on the combination of delay‐bounded wireless link level scheduling and the network layer service vector concept, resulting in significant power savings and finer end‐to‐end QoS granularity. The impact of various traffic arrival distributions and flows with different QoS requirements on the performance of this cross‐layer architecture is also investigated and evaluated. Copyright © 2006 John Wiley & Sons, Ltd.

A novel distributed end-to-end quality-of-service (QoS) provisioning architecture based on the concept of decoupling the end-to-end QoS provisioning from the service provisioning at routers in the differentiated service (DiffServ) network is proposed. The main objective of this architecture is to enhance the QoS granularity and flexibility offered in the DiffServ network model and improve both the network resource utilization and user benefits. The proposed architecture consists of a new endpoint admission control referred to as explicit endpoint admission control at the user side, the service vector which allows a data flow to choose different services at different routers along its data path, and a packet marking architecture and algorithm at the router side. The achievable performance of the proposed approach is studied, and the corresponding results demonstrate that the proposed mechanism can have better service differentiation capability and lower request dropping probability than the integrated service over DiffServ schemes. Furthermore, it is shown that it preserves a friendly networking environment for conventional transmission control protocol flows and maintains the simplicity feature of the DiffServ network model.

In cellular networks, the implementation of various resource management processes, such as bandwidth reservation and location updates, has been based on the one-to-one resource management information exchange paradigm, between the mobile nodes and the base stations. In this paper, we design and demonstrate the use of a distributed cooperative scheme that can be applied in the future wireless networks to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer-to-peer communication concept of wireless ad hoc networks. In our approach, the network is subdivided into one-hop ad hoc clusters where the members of each cluster cooperate to perform the required management functions, and conventional individual direct report transmissions of the mobile terminals to the base stations are replaced by two-hop transmissions. The performance evaluation and the corresponding numerical results presented in this paper confirm that our proposed scheme reduces significantly the overall system energy consumption when compared with the conventional one-to-one direct information management exchange approach. Furthermore the issue of fairness in dynamically selecting the various cluster heads in successive operational cycles of the proposed scheme is analyzed, and an enhanced algorithm is proposed and evaluated, which improves significantly the cluster head selection fairness, in order to balance the energy consumption among the various mobile terminals. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper, a novel concept of decoupling the end-to-end QoS provisioning from the service provisioning at routers in the Diffserv network is proposed to enhance the QoS granularity offered in the Diffserv model and improve both the network resource utilization and user benefits. To realize the concept, we implement a new endpoint admission control, referred to as explicit endpoint admission control, with the service vector concept at the user side, which allows a data flow to choose different services at different routers. At the router side, we propose a new packet marking scheme, by which the end host can obtain the performance of each service class at each router and determine the service vector. The achievable performance of the proposed approach is studied and the corresponding results demonstrate that the proposed mechanism can have better service differentiation capability and lower request dropping probability than the Intserv over Diffserv schemes while it still maintains the simplicity feature of the Diffserv network model.

The random access scheme has been shown to be an efficient transfer mechanism of packet data in wireless environments for nonreal-time applications where messages with variable length are transmitted over power controlled code-division multiple-access (CDMA) channels. Most of the previous work in this area covered the analysis of the CDMA systems for fixed or exponential packet length, infinite population, and unlimited waiting and service time where the results mainly depend on the mean values of the traffic. In this paper, we remove these assumptions and analyze the behavior of the system for the more general and realistic case of finite population, finite sojourn time, and general packet length distribution. Specifically, we provide an analytical method to study the performance related parameters of the system, such as packet delay, packet loss, and throughput as well as the effect of packet length distribution on the system performance under a realistic environment. The obtained results demonstrate that for the packet length distributions with the same mean but different tail properties, the system behavior can change dramatically. In addition, we demonstrate that this study provides an analytical tool that can be used as the underlying framework for the support of a wide range of applications and management functions such as optimization of design parameters, integration of multimedia services, and anomaly detection in CDMA wireless networks.

This paper develops an analytical model for evaluating the quality of service (QoS) in wireless ad-hoc networks. In doing so it extends the trunking theory concepts to encompass the effect of co-channel interference. Chosen as the QoS figure of merit, the transmission blocking probability is derived as a function of the number of nodes, the network density and parameters of a Markov chain model for the multiple user channel access protocol. This expression is validated by computer simulations.

Early detection of network failures and performance degradations is a key to rapid fault recovery and robust networking, and has been receiving increasing attention lately. In this paper we present a fault diagnostic methodology, based on the characterization of the dynamic statistical properties of traffic normality in order to detect network anomalies. Anomaly detection is based on the concept that perturbations of normal behavior suggest the presence of faults. In order to design a system that provides an accurate identification of the normal network traffic behavior, we first develop an anomaly-tolerant non-stationary traffic prediction technique, which is capable of removing both single pulse and continuous anomalies. Furthermore we design and introduce dynamic thresholds, and based on them we define adaptive anomaly violation as a combined function of both magnitude and duration of the traffic deviations. Finally numerical results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach.

The position-guided sliding-window routing (PSR) protocol is a scalable single tier routing protocol designed for operation in mobile ad hoc networking environments. In this paper, we provide an analytical model and framework in order to study the various design issues and trade-offs of PSR routing mechanism, discuss their impact on the protocol's operation and effectiveness, identify optimal values for critical design parameters under different mobility scenarios, and finally provide guidelines to optimize the protocol's performance.

In this paper we propose a scalable differentiated service model paradigm to provide fine QoS granularity in Internet via the framework of a distributed admission control scheme referred to as explicit endpoint admission control (EEAC). Specifically, in the proposed EEAC scheme the end host sends out probing packet to the network and makes admission decision according to the probing results that are explicitly provided at each router along the path. By introducing the concept of service vector, the proposed service model paradigm allows users to choose different services at different nodes along the path from the source to the destination to achieve fine QoS granularity and architectural scalability. Models for both users and network service providers to maximize their benefits are also discussed in this paper. The corresponding simulation results demonstrate that the new service model paradigm and EEAC scheme can result in lower request drop ratio, satisfactory end-to-end performance, and lower cost for each user.

The development of architectures and strategies that allow for rapid and cost-effective deployment of large scale geographically distributed sensor based systems that organize themselves in an ad hoc fashion in order to improve the monitoring and detection capabilities, is becoming more a requirement rather than a desire. In this paper, we first provide a model that characterizes the corresponding sensor connectivity distribution for a sensor networking system, and based on this model we gain some insight about the trade off among the node connectivity, power consumption, data rate, etc. The impact of node connectivity on system reliability is discussed. Furthermore in order to reduce the sensor power consumption we analyze the relationship between periodical sleeping strategies and the achieved power conservation. Several results and tradeoffs among various sleeping strategies, transmission scenarios and power gains, for given connectivity requirements, are also presented and evaluated.

In this paper, we study the fundamental problem of allocating with efficiency and fairness the available resources in a code-division multiple-access wireless system that supports multirate multimedia services. Our proposed approach adopts the use of dynamically assigned data rates that match the channel capacity in order to improve the system throughput and overcome the problems associated with the location-dependent and time-dependent errors and channel conditions, the variable system capacity and the transmission power limitation. We introduce and describe two new algorithms, namely the channel adaptive rate scheduling (CARS) and fair channel adaptive rate scheduling (FCARS) algorithms. CARS improves the system throughput by adjusting the transmission rates according to the varying channel conditions and performs an iterative procedure to determine the power index that a user can accept by its current channel condition and transmission power. Based on the assignment of CARS, FCARS achieves the objective of fairness by further compensating the lagging users, while still maintaining all the constraints imposed by the system. The performance evaluation process confirms that our approach achieves, simultaneously, the design objectives of both high throughput and fairness and demonstrate the corresponding improvements.

Abstract The field of mobile ad hoc networking has enjoyed dramatic increase in popularity over the last few years. However, owing to the fact that such networks have dynamic, sometimes rapidly changing, random, multihop topologies, the development of efficient and applicable multicast routing protocols presents many issues and challenges. In this paper we propose a Mobility‐based Hybrid Multicast Routing (MHMR) protocol suitable for mobile ad hoc networks. The main features that our proposed protocol introduces are the following: (i) mobility‐based clustering and group‐based hierarchical structure, in order to effectively support stability and scalability; (ii) group‐based (limited) mesh structure and forwarding tree concepts, in order to support the robustness of the mesh topologies, which provides ‘limited’ redundancy and the efficiency of tree forwarding simultaneously; and (iii) combination of proactive and reactive concepts that provide low route acquisition delay and low overhead. The use of dynamic mobility‐based clustering as the underlying structure is motivated by the observation that in mobile ad hoc networks communications are often among teams that tend to coordinate their movements, and as a result we can dynamically and adaptively partition the network into several groups, each with its own mobility characteristics and behaviors. In our protocol we support the creation of a limited mesh structure based on the clusterheads of the various created clusters, and not among all the members that participate in the multicast session and route, thereby reducing significantly the complexity of the created mesh topology. The performance evaluation of the proposed protocol is achieved via modeling and simulation. The corresponding results demonstrate the proposed multicast protocol's efficiency in terms of packet delivery ratio, scalability, control overhead, end‐to‐end delay as a function of mobility, packet transmission rate, and multicast group size. Copyright © 2003 John Wiley & Sons, Ltd.

The use of fluid generalized processor sharing (GPS) algorithm for integrated services networks has received a lot of attention since early 1990s because of its desirable properties in terms of delay bound and service fairness. Many packet fair queuing (PFQ) algorithms have been developed to approximate GPS. However, owing to their implementation complexity, it is difficult to support a large number of sessions with diverse service rates while maintaining the GPS properties. The grouping architecture has been proposed to dramatically reduce the implementation complexity. However, it can only support a fixed number of service rates, thus causing the problem of granularity. We present a viable implementation of our previously proposed dual-rate grouping architecture, and demonstrate that, as compared with the original grouping architecture, our proposed scheme possesses better performance in terms of approximating per session-based PFQ algorithms without increasing the implementation complexity.

In this paper, we analyze the common packet channel (CPCH) mechanism for finite population, finite sojourn time and general packet length distribution. The obtained results demonstrate that for packet length distributions with the same mean but different tail properties the system behavior can change dramatically. In addition, we demonstrate that this study provides an analytical tool that can be used as the underlying framework for the support of a wide range of applications and management functions such as optimization of design parameters, integration of multimedia services and anomaly detection in CDMA wireless networks.

We propose the use of the optimal least weight routing (OLWR) algorithm for routing QoS flows in high-speed networks. The main principle of our algorithm is that the choice of the most appropriate route, is based on a set of parameters that estimate the impact that, the acceptance and routing decision of a call request belonging to a specific class, would have on the network and other classes of service. The performance evaluation results demonstrated that OLWR outperforms both the multi-hop least-loaded routing algorithms and the multihop most-loaded routing algorithms in terms of both revenue and carried load.

We propose an integrated solution for QoS management in mobile wireless networks that can be dynamically adapted to user requirements and resource availability. Due to the strong variability of the environment under examination, we propose to use mobile agents as the underlying enabling technology. We propose a proactive approach that tries to satisfy the user requirements by utilizing advance resource reservations, and a reactive approach which tries to negotiate network resources when significative changes in the demand of resources arise. We also propose a combination of the two approaches in an integrated hybrid system that combines a mobility predictive advanced bandwidth reservation scheme with a call admission control and resource reconfiguration strategy to support flexible QoS management.

Agent programming technology has emerged as a flexible and complementary way to manage resources of distributed systems due to the increased flexibility in adapting to the dynamically changing requirements of such systems. A very promising application of this technology is related to the control of forthcoming networking systems which will represent a competitive marketplace with a multitude of vendors, operators and customers. Thus, new reference models have to be investigated in order to better satisfy users' requirements in a framework where resource allocation is provided under the control of different and often competing stakeholders (users, network providers, service providers, etc.). We believe that autonomy is one of the features that will characterize the behavior of agents in such environment: autonomous choices will be taken as the result of coordination among different cooperating software entities. Following this direction, we describe the efficient integration and adoption of mobile agents and genetic algorithms in the implementation of a valuable strategy for the development of effective market based routes for brokering purposes in the future multioperator network marketplace. The proposed genetic algorithm provides a kind of stochastic algorithm searching process in order to identify optimal resource allocation strategies. The agent-based network management approach represents an underlying framework and structure for the multioperator network model, and can be used to facilitate the collection and dissemination of the required management data, as well as the efficient and distributed operation of the algorithm. We also present some numerical results to assess the performance and operation effectiveness of our approach, by applying it in some test case scenarios.

We propose an integrated resource management approach that can be implemented in next generation wireless networks that support multimedia services (data, voice, video, etc.). Specifically, we combine the use of position-assisted and mobility predictive advanced bandwidth reservation with a call admission control and bandwidth reconfiguration strategy to support flexible QoS management. We also introduce a mobile agent based framework that can be used to carry out the functions of geolocation and of the proposed resource management in wireless networks. A model is also developed to obtain the optimal location information update interval in order to minimize the total cost of the system operation. The comparison of the achievable performance results of our proposed scheme with the corresponding results of a conventional system that supports advanced bandwidth reservation only, as means of supporting the QoS requirements, demonstrate that our integrated scheme can alleviate the problem of overreservation, support seamless operation throughout the wireless network, and increase significantly the system capacity.

Call admission control (CAC) plays a significant role in providing the desired quality of service (QoS) in cellular networks. We investigate the role of pricing as an additional dimension of the call admission control process in order to efficiently and effectively control the use of wireless network resources. First, we prove that, for a given wireless network, there exists a new call arrival rate which can maximize the total utility of users while maintaining the required QoS. Based on this result and observation, we propose an integrated pricing and call admission control scheme where the price is adjusted dynamically based on the current network conditions in order to alleviate the problem of congestion. Our proposed integrated approach implicitly implements a distributed user-based prioritization mechanism by providing negative incentives according to the current network conditions and therefore shaping the aggregate traffic in the network. We compare the performance of our approach in terms of congestion prevention, achievable total user utility, and obtained revenue, with the corresponding results of conventional systems where pricing is not taken into consideration in the call admission control process. These performance results verify the considerable improvement that can be achieved by the integration of pricing in the call admission control process in cellular networks.

Abstract The basic philosophy of personal communication services is to provide user‐to‐user, location independent communication services. The emerging group communication wireless applications, such as multipoint data dissemination and multiparty conferencing tools have made the design and development of efficient multicast techniques in mobile ad‐hoc networking environments a necessity and not just a desire. Multicast protocols in mobile ad‐hoc networks have been an area of active research for the past couple of years. This paper summarizes the activities and recent advances in this work‐in‐progress area by identifying the main issues and challenges that multicast protocols are facing in mobile ad‐hoc networking environments, and by surveying several existing multicasting protocols. This article presents a classification of the current multicast protocols, discusses the functionality of the individual existing protocols, and provides a qualitative comparison of their characteristics according to several distinct features and performance parameters. Furthermore, since many of the additional issues and constraints associated with the mobile ad‐hoc networks are due, to a large extent, to the attribute of user mobility, we also present an overview of research and development efforts in the area of group mobility modeling in mobile ad‐hoc networks. Copyright © 2001 John Wiley & Sons, Ltd.

In this paper we propose a scheme referred to as dual-rate session grouping to improve the service rate granularity for cell-based schedulers. In this scheme a session is split into two subsessions to provide the average service rate that a user requires. By applying dual-rate session grouping, the utilization of bandwidth and fairness among users can be improved, while the complexity of the scheduling algorithm remains the same as the conventional scheme. The overall computational complexity of the dual-rate session grouping does not increase with the rate granularity that is only limited by the available memory space. Several implementation issues are also presented in this paper.

Channel reservation techniques have been extensively used in cellular networks in order to meet the quality of service (QoS) requirements. An influence-based channel reservation and call admission control scheme is proposed to provide the QoS guarantee in a mobile wireless network. The basic idea behind the proposed scheme is that a moving user; in addition to its requirements in the current cell, exerts some influence on the channel allocation in neighboring cells. Such an influence is related to the moving pattern of this user (speed and direction), and it can be calculated statistically. We first introduce the concept of the influence curve, which provides an estimate of the requirements that the ongoing calls in current cell will impose on a neighboring cell. Based on this concept we propose a channel reservation scheme that dynamically and adaptively adjusts the number of channels that should be reserved for handoff purposes in each cell. The proposed algorithm can be carried out in a distributed way: each cell collects its current traffic condition, calculates the influence and sends the results to all its neighbors periodically. The performance evaluation of the proposed scheme is achieved via analysis and simulation.

This paper analyzes the performance of unslotted CDMA random access schemes with heavy-tailed packet length distribution. Most of the previous work cover the analysis of the system with Poisson process and for infinite population and infinite buffer size where the results mainly depend on the mean values of the traffic. We remove these assumptions and analyze the behavior of the system for the more general and realistic case of finite population, finite buffers and variable packet length. The emphasis is placed on the study of the effect of the heavy-tailed packet length on the system characteristics. Specifically, we show via analysis and simulation that even when the mean packet length is small the system can have bottlenecks due to the packet length characteristics, and therefore dynamic adaptation and control of the system is required.

An agent based approach is investigated to build a framework where resource allocation is provided under the control of different and often competing stake-holders (users, network providers, service providers, etc.). This paper also describes the efficient integration and adoption of mobile agents and genetic algorithms in the implementation of an effective strategy for the development of effective market based routes for brokering purposes in the future multi-operator network marketplace. The agent-based network management approach represents an underlying framework and structure for the multi-operator network model, and can be used to collect all the required management data. The proposed genetic algorithm provides a kind of stochastic algorithm searching process in order to identify optimal resource allocation strategies.

Traditional call admission control (CAC) schemes that mainly focus on the trade off between new call blocking probability and handoff call blocking probability can not solve the problem of congestion in wireless networks. We investigate the role of pricing as an additional dimension of the call admission control process in order to efficiently and effectively control the use of wireless network resources. First we prove that for a given wireless network there exists a new call arrival rate which can maximize the total utility of users. Based on this result and observation we propose an integrated pricing and call admission control scheme, where the price is adjusted dynamically based on the current network conditions, in order to alleviate the problem of congestion. We compare the performance of our approach with the corresponding results of conventional systems where pricing is not taken into consideration in the call admission control process. These performance results verify the considerable improvement that can be achieved by the integration of pricing in the call admission control process in cellular networks.

Applying TCP to wireless networks gives rise to a series of problems because of the intrinsic characteristic of the TCP flow control mechanism. We study the problem in mobile ad-hoc networks where wireless mobile hosts communicate with each other in the absence of a fixed infrastructure. In general in wireless mobile ad hoc networks, the packet losses are mainly due to one of the following reasons: (1) route failure/change due to change of network topology; (2) high link error rate; (3) congestion. Each mobile host emits a beacon signal that is used to identify itself and notify its neighbors about its existence. We use this method as an indication for the TCP connection to improve its performance. To accomplish it, we introduce and design a new layer, namely the link signal strength agent (LSSA) layer. The initial performance results indicate that the proposed new approach overcomes the problems associated with the nature of the wireless links and the mobility of nodes and therefore is applicable in dynamic wireless networks.

In this paper we propose a new algorithm for routing in mobile survivable networks, based on the combination of position-based routing concepts and fault-tolerant routing techniques in computer networks. By using a combination of these two concepts we achieve a simplified way of localizing routing overhead while at the same time we improve the operational effectiveness of the position-based routing approaches by alleviating some of the drawbacks associated with them, such as routing deadlock occurrences, and therefore creating a robust and fault tolerant routing strategy.

We describe the design and implementation of automated methodologies and tools that facilitate the implementation of an enhanced maintenance process in order to provide improved quality of service offered to the customers for the transaction access services (TAS) network. The emphasis of our work is placed on the identification, filtering and correlation of event occurrences that indicate problems that do not necessarily generate alarmed conditions on the conventional network management systems, and on the proactive service/fault management and detection based on dynamically defined violations of the base-lined performance profiles. Such an approach enhances considerably the network management and maintenance process, by identifying possible situations/incidents that may affect the network performance, and by correlating those incidents to the potentially affected elements, services and customer applications.

Proactive detection of network failures and performance degradations is a key to rapid fault recovery and thus robust networking. The authors present methodologies and algorithms that were developed in order to enhance the proactive and adaptive detection of network/service anomalies (failures and performance degradations) in transaction based electronic commerce wide area networks (WANs). Specifically our proactive network/service anomaly detection method detects network/service performance degradations and failures in multiple service class networks, where performances of service classes are mutually dependent and strongly correlated, and where external or environmental factors (e.g., non-managed or non-monitored equipment within customer premises) can strongly impact network and service performances. The authors describe and implement algorithms that: (1) sample and convert raw transaction records to service class based performance data in which potential network anomalies are highlighted; (2) construct adaptive and service class based performance thresholds for real time detection of network and service anomalies; and (3) perform real time network anomaly detection.

We present two decentralized iterative power control algorithms (IPCAs) for the uplink and downlink communication in multi-service CDMA wireless environments in order to expand the system capacity while at the same time satisfy the various QoS requirements. The proposed dynamic IPCAs are decentralised in the sense that they can be implemented in a distributed mode at the individual cell sites and mobile stations based on local measurements and information, and without the need of co-ordination between the different cells. The main feature of these two power control algorithms is that they combine the allocation as well as the correction of power in a multi-service wireless system. The formulation of the power control problem as presented in this paper, as well as the proposed algorithms, are especially applicable in multi-service wireless environments that support several classes of applications where each class has different quality of service requirements. Finally we present some numerical results that show that the convergence of the algorithm is very fast and the powers approach the optimal values within a small number of iterations.

This paper deals with the performance evaluation of a scalable single-tier routing protocol designed for operation in mobile ad hoc networking environments. The protocol under consideration is based on the superposition of link-state and position-based routing, and it employs a simplified way of localizing routing overhead, without having to resort to complex, multiple-tier routing organization schemes. Specifically we study the protocol's scalability measured by the routing overhead, by calculating the average number of routing packets per node, as a function of the network size. The obtained results demonstrate that the average number of routing packets transmitted by a node is practically insensitive to the network size. This clearly indicates that our protocol possesses the scalability property which is very critical in mobile ad hoc networking environments, without having to resort to complicated and vulnerable hierarchical approaches. Moreover we compare the performance of our protocol with a non-realistic "ideal routing protocol" where packets are routed assuming continuous and accurate knowledge of the exact node position. Specifically we study the "route distortion" introduced by our protocol due to inaccuracies in the positions of other hosts known by the local host, and compare the number of hops needed to route data under our approach against the corresponding results obtained for the "ideal routing". The results indicate that for various mobility scenarios our protocol has nearly optimum data routing characteristics. Finally we discuss various design parameters related to the protocol's operation and we investigate their impact on the protocol's performance and effectiveness.

The motivation of the work presented in this paper comes from a real network management center in charge of supervising a very large hybrid telecommunications/data transaction-oriented network. We present a set of tools that we have developed and implemented in the AT&T Transaction Access Services (TAS) network, in order to automate and facilitate the process of diagnosing network faults and identifying the potentially affected elements, resources and customers. Specifically in this paper we describe the development implementation and use of the following systems: (a) the TAS Information and Tracking System (TIMATS) that provides a common framework for the storage and retrieval of provisioning, capacity management and maintenance data; (b) the Transactions Event Viewer (TEVIEW) system that generates, filters, and presents diagnostic events that indicate system occurrences or conditions that may cause a degradation of the service; and (c) the Transaction Instantaneous Anomaly Notification (TRISTAN) system which implements an adaptive network anomaly detection software that detects network and service anomalies of TAS as dynamically defined violations of the base-lined performance characteristics and profiles.

Algorithms and software for proactive and adaptive detection of network/service anomalies (i.e., performance degradations) have been developed, implemented, deployed, and field-tested for transaction-oriented wide area networks (WANs). A real-time anomaly detection system called TRISTAN (transaction instantaneous anomaly notification) has been implemented, and is deployed in the commercially important AT&T transaction access services (TAS) network. TAS is a high volume, multiple service classes, hybrid telecom and data WAN that services transaction traffic in the U.S. and neighboring countries. TRISTAN adaptively and preactively detects network/service performance anomalies in multiple-service-class-based and transaction-oriented networks, where performances of service classes are mutually dependent and correlated, where environmental factors (e.g., nonmanaged or nonmonitored equipment within customer premises) can strongly impact network and service performances. Specifically, TRISTAN implements algorithms that: 1) sample and convert raw transaction records to service-class based performance data in which potential network anomalies are highlighted; 2) automatically construct adaptive and service-class-based performance thresholds from historical transaction records for detecting network and service anomalies; and 3) perform real-time network/service anomaly detection. TRISTAN is demonstrated to be capable of proactively detecting network/service anomalies, which easily elude detection by the traditional alarm-based network monitoring systems.

Algorithms and online software for automated and adaptive detection of network/service anomalies have been developed and field-tested for transaction-oriented wide area networks (WAN). These transaction networks are integral parts of electronic commerce infrastructures. Our adaptive network/service anomaly detection algorithms are demonstrated in a commercially important production WAN, currently monitored by our recently implemented real-time software system, TRISTAN (transaction instantaneous anomaly notification). TRISTAN adaptively and proactively detects network/service performance degradations and failures in multiple service-class transaction-oriented networks, where performances of service classes are mutually dependent and correlated, and where external or environmental factors can strongly impact network and service performances. In this paper, we present the architecture, summarize the implemented algorithms, and describe the operation of TRISTAN as deployed in the AT&T transaction access services (TAS) network. TAS is a commercially important, high volume, multiple service classes, hybrid telecommunication and data WAN that services transaction traffic in the USA and neighboring countries. It is demonstrated that TRISTAN detects network/service anomalies in TAS effectively. TRISTAN can automatically and dynamically detect network/service faults, which can easily elude detection by the traditional alarm-based network monitoring systems.

This paper focuses on the effective management of networks that support online services that require high availability and reliability, and quick reconfiguration response and fast reconstitution time, in the event of failures. In a service independent network, the network provider provides the network assets to the service provider. In such a paradigm, the service provider, for example an Internet service provider (ISP) would like to monitor the health of the network in a proactive manner. From the service provider's view the domain of interest usually includes link failures, node failures, local dial numbers impacted, capacity and resulting congestion. We describe algorithms and methodologies for translating faults to the various impacted objects and analyze the capacity impact due to the corresponding failures. We also describe the network management architecture typically used to deliver and distribute the results of those algorithms. Finally, we present some preliminary qualitative results on the application of methods that we are currently developing in order to build dynamic performance profiles ("signatures") of application-based traffic intensities, and therefore provide enhanced and intelligent on-line network analysis and control, for value-added on-line type of services (i.e. transaction access services).

The provision of personal communication services is the goal of the evolution of integrated communication systems. The fundamental problem underlying any phase (hand-off, new connection, etc.) of a dynamic resource allocation algorithm in a wireless network is to assign transmission powers, forward (downstream) and reverse (upstream) channels, and base stations such that every mobile of the system can establish a connection. Each one of these problems separately has been studied extensively. We consider the joint problem in a system with two base stations. An algorithm that achieves the optimal assignment is provided. It involves the computation of a maximum matching in a graph that captures the topological characteristics of the mobile locations. The traffic capacities, in terms of expected number of connections per channel, of the forward and reverse channel are obtained and compared, for both cases of power control and nonpower control. It turns out that when the transmission power is fixed, the capacities of the forward and reverse channel are different, while when power control is allowed they are the same. For systems with two mobiles the capacities of the forward and reverse channels are studied analytically. Finally, several versions of the two-way channel assignment problem are studied.

Reuse partitioning is a technique for providing more efficient spectrum reuse in cellular radio systems. A cell in such a system is divided into concentric zones, each associated with an overlaid cell plan. Calls that arise in the periphery of the cell have fewer channels in their availability than those arising close to the base station and therefore they experience higher blocking rates. The authors consider the problem of balancing uniformly the blocking probability throughout the cell offering a fair treatment to the whole area within the cell, by controlling the allocation do the different channel layers. A policy that minimizes the maximum blocking probability experienced at any location of the cell is identified and is shows to be of threshold type, An adaptive scheme that adjusts the threshold based on estimates of the blocking probabilities in the different zones of the cell as proposed. This scheme tracks the optimal threshold effectively without any knowledge of the traffic parameters. Simulation study shows that substantial capacity improvements care achieved by the application of the optimal channel assignment policy, over the uncontrolled system.>

Reuse partitioning is a technique for providing more efficient spectrum reuse in cellular radio systems. A cell in such a system is divided into concentric zones, each associated with an overlaid cell plan. Calls that arise in the periphery of the cell have fewer channels in their availability than those arising close to the base station and therefore they experience higher blocking rates. In this paper we consider the problem of balancing uniformly the blocking probability throughout the cell offering a fair treatment to the whole area within the cell, by controlling the allocation to the different channel layers. A policy that minimizes the maximum blocking probability experienced at any location of the cell is identified and is shown to be of threshold type. The policy satisfies any achievable constraint on the blocking rate uniformly throughout the cell. An adaptive scheme that adjusts the threshold based on estimates of the blocking probabilities in the different zones of the cell is proposed. This scheme tracks the optimal threshold effectively without any knowledge of the traffic parameters. Simulation study shows that substantial capacity improvements are achieved by the application of the optimal channel assignment policy, over the uncontrolled system.>

Computer communication vendors and users are faced with growing communication systems that must handle voice, data, and soon, video on an integrated basis, eventually through ISDN. Vendors and users must somehow control the operation of this entire system to satisfy customer needs at reasonable cost, by means of sophisticated network management and control systems. Our objective is to identify degraded