20 research outputs found
Tools and Algorithms for the Construction and Analysis of Systems
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
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
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
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
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
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Spatial stochastic models for network analysis
This thesis proposes new stochastic interacting particle models for networks, and studies some fundamental properties of these models. This thesis considers two application areas of networking - engineering design questions in future wireless systems and algorithmic tasks in large scale graph structured data. The key innovation introduced in this thesis is to bring tools and ideas from stochastic geometry to bear on the problems in both these application domains. We identify certain fundamental questions in design and engineering both wireless systems and large scale graph structured data processing systems. Subsequently, we identify novel stochastic geometric models, that captures the fundamental properties of these networks, which forms the first research contribution. We then rigorously study these models, by bringing to bear new tools from stochastic geometry, random graphs, percolation and Markov processes to establish structural results and fundamental phase transitions in these models. Using our developed mathematical methodology, we then identify design insights and develop algorithms, which we demonstrate are instructive in many practical settings. In the setting of wireless systems, this thesis studies both ad-hoc and cellular networks. In the ad-hoc network setting, we aim to understand fundamental limits of the simplest possible protocol to access the spectrum, namely a link transmits whenever it has data to send by treating all interference as noise. Surprisingly this basic question itself was not understood, as the system dynamics is coupled spatially due to the interference links cause one another and temporally due to randomness in traffic arrivals. We propose a novel interacting particle model called the spatial birth-death wireless network model to understand the stability properties of the simple spectrum access protocol. Using tools from Palm calculus and fluid limit theory, we establish a tight characterization of when this model is stable. Furthermore, we show that whenever stable, the links in steady-state exhibit a form of clustering. Leveraging these structural results, we propose two mean field heuristics to obtain formulas for key performance metrics such as average delay experienced by a link. We empirically find that the proposed formulas for delay predicts accurately the system behavior. We subsequently study scalability properties of this model by introducing an appropriate infinite dimensional version of the model we call the Interference Queueing Networks model. The model consists of a queue located at each grid point of an infinite regular integer lattice, with the queues interacting with each other in a translation invariant fashion. We then prove several structural properties of the model namely, tight conditions for existence of stationary solutions and some sufficient conditions for uniqueness of stationary solutions. Remarkably, we obtain exact formula for mean delay in this model, unlike the continuum model where we relied on mean-field type heuristics to obtain insights. In the setting of cellular networks, we study optimal association schemes by mobile phones in the case when there are several possible base station technologies operating on orthogonal bands. We show that this choice leads to a performance gain we term technology diversity. Interestingly, we show that the performance gain relies on the amount of instantaneous information a user has on the various base station technologies that it can leverage to make the association decision. We outline optimal association schemes under various information settings that a user may have on the network. Moreover, we propose simple heuristics for association that relies on a user obtaining minimal instantaneous information and are thus practical to implement. We prove that in certain natural asymptotic regime of parameters, our proposed heuristic policy is also optimal, and thus quantifying the value of having fine grained information at a user for association. We empirically observe that the asymptotic result is valid even at finite parameter regimes that are typical in todays networks. In the application of analyzing large scale graph structured data, we consider the graph clustering problem with side information. Graph clustering is a standard and widely used task which consists in partitioning the set of nodes of a graph into underlying clusters where nodes in the same cluster are similar to each other and nodes across different clusters are different. Motivated by applications in social and biological networks, we consider the task of clustering nodes of a graph, when there is side information on the nodes, other than that contained in the graph. For instance in social networks, one has access to meta data about a person (node in a social graph) such as age, location, income etc, along with the combinatorial data of who are his friends on the social graph. Similarly, in biological networks, there is often meta-data about an experiment that provides additional contextual data about a node, in addition to the combinatorial data. In this thesis, we propose a generative model for such graph structured data with side information, which is inspired by random graph models in stochastic geometry such as the random connection model and the generative models for networks with clusters without contexts, such as the stochastic block model or the planted partition model. We propose a novel graph model called the planted partition random connection model. Roughly speaking, in this model, each node has two labels - an observable R [superscript d] valued (for some fixed d) feature label and an unobservable binary valued community label. Conditional on the node labels, edges are drawn at random in this graph depending on both the feature and community labels of the two end points. The clustering task consists in recovering the underlying partition of nodes corresponding to the respective community labels better than a random assignment, when given an observation of the graph generated and the features of all nodes. We show that if the 'density of nodes', i.e., average number of nodes having features in an unit volume of space of R [superscript d] is small, then no algorithm can cluster the graph that can asymptotically beat a random assignment of community labels. On the contrary, if the density of nodes is sufficiently high, we give a simple algorithm that recovers the true underlying partition strictly better a random assignment. We then apply the proposed algorithm to a problem in computational biology called Haplotype Phasing and observe empirically, that it obtains state of art results. This demonstrates, both the validity of our generative model, as well as our new algorithm.Electrical and Computer Engineerin
Multi-attribute demand characterization and layered service pricing
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
We consider the problem of resolving contention in communication networks
with selfish users. In a \textit{contention game} each of identical
players has a single information packet that she wants to transmit using one of
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 and , 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. , with
probability tending to 1 as .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
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