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- Flärdh, Oscar, 1980-
(författare)
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Modelling, analysis and experimentation of a simple feedback scheme for error correction control
- 2007
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data networks are an important part in an increasing number of applications with real-time and reliability requirements. To meet these demands a variety of approaches have been proposed. Forward error correction, which adds redundancy to the communicated data, is one of them. However, the redundancy occupies communication bandwidth, so it is desirable to control the amount of redundancy in order to achieve high reliability without adding excessive communication delay. The main contribution of the thesis is to formulate the problem of adjusting the redundancy in a control framework, which enables the dynamic properties of error correction control to be analyzed using control theory. The trade-off between application quality and resource usage is captured by introducing an optimal control problem. Its dependence on the knowledge of the network state at the transmission side is discussed. An error correction controller that optimizes the amount of redundancy without relying on network state information is presented. This is achieved by utilizing an extremum seeking control algorithm to optimize the cost function. Models with varying complexity of the resulting feedback system are presented and analyzed. Conditions for convergence are given. Multiple-input describing function analysis is used to examine periodic solutions. The results are illustrated through computer simulations and experiments on a wireless sensor network.
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- Johansson, Björn, 1978-
(författare)
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Distributed resource allocation in networked systems using decomposition techniques
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The Internet and power distribution grids are examples of ubiquitous systems that are composed of subsystems that cooperate using a communication network. We loosely define such systems as networked systems. These systems are usually designed by using trial and error. With this thesis, we aim to fill some of the many gaps in the diverse theory of networked systems. Therefore, we cast resource allocation in networked systems as optimization problems, and we investigate a versatile class of optimization problems. We then use decomposition methods to devise decentralized algorithms that solve these optimization problems. The thesis consists of four main contributions: First, we review decomposition methods that can be used to devise decentralized algorithms for solving the posed optimization problems. Second, we consider cross-layer optimization of communication networks. Network performance can be increased if the traditionally separated network layers are jointly optimized. We investigate the interplay between the data sending rates and the allocation of resources for the communication links. The communication networks we consider have links where the data transferring capacity can be controlled. Decomposition methods are applied to the design of fully distributed protocols for two wireless network technologies: networks with orthogonal channels and network-wide resource constraints, as well as wireless networks using spatial-reuse time division multiple access. Third, we consider the problem of designing a distributed control strategy such that a linear combination of the states of a number of vehicles coincide at a given time. The vehicles are described by linear difference equations and are subject to convex input constraints. It is demonstrated how primal decomposition techniques and incremental subgradient methods allow us to find a solution in which each vehicle performs individual planning of its trajectory and exchanges critical information with neighbors only. We explore various communication, computation, and control structures. Fourth, we investigate the resource allocation problem for large-scale server clusters with quality-of-service objectives, in which key functions are decentralized. Specifically, the problem of selecting which services the servers should provide is posed as a discrete utility maximization problem. We develop an efficient centralized algorithm that solves this problem, and we propose three suboptimal schemes that operate with local information.
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- Johansson, Björn, 1978-
(författare)
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On Distributed Optimization in Networked Systems
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Numerous control and decision problems in networked systems can be posed as optimization problems. Examples include the framework of network utility maximization for resource allocation in communication networks, multi-agent coordination in robotics, and collaborative estimation in wireless sensor networks (WSNs). In contrast to classical distributed optimization, which focuses on improving computational efficiency and scalability, these new applications require simple mechanisms that can operate under limited communication. In this thesis, we develop several novel mechanisms for distributed optimization under communication constraints, and apply these to several challenging engineering problems. In particular, we devise three tailored optimization algorithms relying only on nearest neighbor, also known as peer-to-peer, communication. Two of the algorithms are designed to minimize a non-smooth convex additive objective function, in which each term corresponds to a node in a network. The first method is an extension of the randomized incremental subgradient method where the update order is given by a random walk on the underlying communication graph, resulting in a randomized peer-to-peer algorithm with guaranteed convergence properties. The second method combines local subgradient iterations with consensus steps to average local update directions. The resulting optimization method can be executed in a peer-to-peer fashion and analyzed using epsilon-subgradient methods. The third algorithm is a center-free algorithm, which solves a non-smooth resource allocation problem with a separable additive convex objective function subject to a constant sum constraint. Then we consider cross-layer optimization of communication networks, and demonstrate how optimization techniques allow us to engineer protocols that mimic the operation of distributed optimization algorithms to obtain an optimal resource allocation. We describe a novel use of decomposition methods for cross-layer optimization, and present a flowchart that can be used to categorize and visualize a large part of the current literature on this topic. In addition, we devise protocols that optimize the resource allocation in frequency-division multiple access (FDMA) networks and spatial reuse time-division multiple access (TDMA) networks, respectively. Next we investigate some variants of the consensus problem for multi-robot coordination, for which it is usually standard to assume that agents should meet at the barycenter of the initial states. We propose a negotiation strategy to find an optimal meeting point in the sense that the agents' trajectories to the meeting point minimize a quadratic cost criterion. Furthermore, we also demonstrate how an augmented state vector can be used to boost the convergence rate of the standard linear distributed averaging iterations, and we present necessary and sufficient convergence conditions for a general version of these iterations. Finally, we devise a generic optimization software component for WSNs. To this end, we implement some of the most promising optimization algorithms developed by ourselves and others in our WSN testbed, and present experimental results, which show that the proposed algorithms work surprisingly well.
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