20 research outputs found

    Tools and Algorithms for the Construction and Analysis of Systems

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    This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

    Efficient Discovery and Utilization of Radio Information in Ultra-Dense Heterogeneous 3D Wireless Networks

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    Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization and network planning replacing them with more accurate 3-Dimensional (3D) network concepts while utilizing spatially distributed location-specific radio characteristics. Empowering this initiative, initially a framework is developed to accurately estimate the location-specific path loss parameters under dynamic environmental conditions in a 3D small cell (SC) heterogeneous networks (HetNets) facilitating efficient radio resource management schemes using crowdsensing data collection principle together with Linear Algebra (LA) and machine learning (ML) techniques. According to the results, the gradient descent technique is with the highest path loss parameter estimation accuracy which is over 98%. At a latter stage, receive signal power is calculated at a slightly extended 3D communication distances from the cluster boundaries based on already estimated propagation parameters with an accuracy of over 74% for certain distances. Coordination in both device-network and network-network interactions is also a critical factor in efficient radio resource utilization while meeting Quality of Service (QoS) requirements in heavily congested future 3D SCs HetNets. Then, overall communication performance enhancement through better utilization of spatially distributed opportunistic radio resources in a 3D SC is addressed with the device and network coordination, ML and Slotted-ALOHA principles together with scheduling, power control and access prioritization schemes. Within this solution, several communication related factors like 3D spatial positions and QoS requirements of the devices in two co-located networks operated in licensed band (LB) and unlicensed band (UB) are considered. To overcome the challenge of maintaining QoS under ongoing network densification and with limited radio resources cellular network traffic is offloaded to UB. Approximately, 70% better overall coordination efficiency is achieved at initial network access with the device network coordinated weighting factor based prioritization scheme powered with the Q-learning (QL) principle over conventional schemes. Subsequently, coverage information of nearby dense NR-Unlicensed (NR-U) base stations (BSs) is investigated for better allocation and utilization of common location-specific spatially distributed radio resources in UB. Firstly, the problem of determining the receive signal power at a given location due to a transmission done by a neighbor NR-U BS is addressed with a solution based on a deep regression neural network algorithm enabling to predict receive signal or interference power of a neighbor BS at a given location of a 3D cell. Subsequently, the problem of efficient radio resource management is considered while dynamically utilizing UB spectrum for NR-U transmissions through an algorithm based on the double Q-learning (DQL) principle and device collaboration. Over 200% faster algorithm convergence is achieved by the DQL based method over conventional solutions with estimated path loss parameters

    Reinforcement Learning-based Optimization of Multiple Access in Wireless Networks

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    In this thesis, we study the problem of Multiple Access (MA) in wireless networks and design adaptive solutions based on Reinforcement Learning (RL). We analyze the importance of MA in the current communications scenery, where bandwidth-hungry applications emerge due to the co-evolution of technological progress and societal needs, and explain that improvements brought by new standards cannot overcome the problem of resource scarcity. We focus on resource-constrained networks, where devices have restricted hardware-capabilities, there is no centralized point of control and coordination is prohibited or limited. The protocols that we optimize follow a Random Access (RA) approach, where sensing the common medium prior to transmission is not possible. We begin with the study of time access and provide two reinforcement learning algorithms for optimizing Irregular Repetition Slotted ALOHA (IRSA), a state-of-the-art RA protocol. First, we focus on ensuring low complexity and propose a Q-learning variant where learners act independently and converge quickly. We, then, design an algorithm in the area of coordinated learning and focus on deriving convergence guarantees for learning while minimizing the complexity of coordination. We provide simulations that showcase how coordination can help achieve a fine balance, in terms of complexity and performance, between fully decentralized and centralized solutions. In addition to time access, we study channel access, a problem that has recently attracted significant attention in cognitive radio. We design learning algorithms in the framework of Multi-player Multi-armed Bandits (MMABs), both for static and dynamic settings, where devices arrive at different time steps. Our focus is on deriving theoretical guarantees and ensuring that performance scales well with the size of the network. Our works constitute an important step towards addressing the challenges that the properties of decentralization and partial observability, inherent in resource-constrained networks, pose for RL algorithms

    Mobile Ad-Hoc Networks

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    Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

    Emerging Communications for Wireless Sensor Networks

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    Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide

    Multi-attribute demand characterization and layered service pricing

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    As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare

    Strategic Contention Resolution in Multiple Channels

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    We consider the problem of resolving contention in communication networks with selfish users. In a \textit{contention game} each of n≥2n \geq 2 identical players has a single information packet that she wants to transmit using one of k≥1k \geq 1 multiple-access channels. To do that, a player chooses a slotted-time protocol that prescribes the probabilities with which at a given time-step she will attempt transmission at each channel. If more than one players try to transmit over the same channel (collision) then no transmission happens on that channel. Each player tries to minimize her own expected \textit{latency}, i.e. her expected time until successful transmission, by choosing her protocol. The natural problem that arises in such a setting is, given nn and kk, to provide the players with a common, anonymous protocol (if it exists) such that no one would unilaterally deviate from it (equilibrium protocol). All previous theoretical results on strategic contention resolution examine only the case of a single channel and show that the equilibrium protocols depend on the feedback that the communication system gives to the players. Here we present multi-channel equilibrium protocols in two main feedback classes, namely \textit{acknowledgement-based} and \textit{ternary}. In particular, we provide equilibrium characterizations for more than one channels, and give specific anonymous, equilibrium protocols with finite and infinite expected latency. In the equilibrium protocols with infinite expected latency, all players transmit successfully in optimal time, i.e. Θ(n/k)\Theta(n/k), with probability tending to 1 as n/k→∞n/k \to \infty.Comment: The results of this work are included in the 11th International Symposium on Algorithmic Game Theory (SAGT 2018) and the 16th Workshop on Approximation and Online Algorithms (WAOA 2018

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

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    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs
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