We propose algorithms that compute polygon approximations for convex contours. This geometric problem is relevant in interpolation theory, data compression, and has potential applications in robotic sensor networks. T...
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ISBN:
(纸本)1424401704;9781424401703
We propose algorithms that compute polygon approximations for convex contours. This geometric problem is relevant in interpolation theory, data compression, and has potential applications in robotic sensor networks. The algorithms are based on simple feedback ideas, on limited nearest-neighbor information, and amount to gradient descent laws for appropriate cost functions. The approximations are based on intuitive performance metrics, such as the area of the inner, outer, and "outer minus inner" approximating polygons
In this paper, we explore to solve the linear equation problem Aθ = b under a directed graph. A “consen-sus+innovation” type distributed algorithm is proposed. In order to promote the convergence speed, we make the...
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In this paper, we explore to solve the linear equation problem Aθ = b under a directed graph. A “consen-sus+innovation” type distributed algorithm is proposed. In order to promote the convergence speed, we make the weight of consensus and innovation be different. By using the proposed algorithm, we can solve any linear equation with at least one solution at a geometric rate from arbitrary initializations. We also propose a new “consensus+residual” algorithm which replace the innovation by the residual and can speed up the iterations.
The authors present a distributed table-filling algorithm for point-to-point routing in a degraded hypercube system. This algorithm finds the shortest length existing path from each source to each destination in the f...
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The authors present a distributed table-filling algorithm for point-to-point routing in a degraded hypercube system. This algorithm finds the shortest length existing path from each source to each destination in the faulty hypercube and fills the routing tables so that messages are routed along these paths. A novel scheme for broadcast routing with tables is proposed, and the algorithm required to fill the broadcast tables, given the point-to-point routing tables, is presented. In addition, the modifications necessary to make these algorithms ensure deadlock-free routing are given. A quantitative and equalitative comparison of previously proposed reroute strategies with table routing, where the tables are filled by the authors' algorithms, are presented.< >
In this paper we consider the problem of maximizing wireless network capacity (a.k.a. one-shot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in...
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ISBN:
(纸本)9781424458363
In this paper we consider the problem of maximizing wireless network capacity (a.k.a. one-shot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in the physical model, and show how they can be generalized to more complicated metrics and settings in which the physical assumptions are slightly violated. We also give the first algorithms in the protocol model that do not assume transmitters can coordinate with their neighbors in the interference graph, so every transmitter chooses whether to broadcast based purely on local events. Our techniques draw heavily from algorithmic game theory and machine learning theory, even though our goal is a distributed algorithm. Indeed, our main results allow every transmitter to run any algorithm it wants, so long as its algorithm has a learning-theoretic property known as no-regret in a game-theoretic setting.
When designing distributed algorithms for application overlay networks, it is usually assumed that the overlay nodes are cooperative to collectively achieve optimal global performance properties. However, this assumpt...
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When designing distributed algorithms for application overlay networks, it is usually assumed that the overlay nodes are cooperative to collectively achieve optimal global performance properties. However, this assumption does not hold in reality, as nodes generally tend to be noncooperative and always attempt to maximize their gains by optimizing their strategies. With such an assumption, we present extensive theoretical analysis to gain insights from a game theoretic perspective, with respect to the behavior of nodes and the equilibrium of the system. The main idea in our analysis is to design appropriate payoff functions, so that the equilibrium of the system may achieve the optimal properties that we desire. Driven by the per-node goal of maximizing gains, such payoff functions naturally lead to distributed algorithms that lead to the desired favorable properties of overlay networks.
This demonstration showcases distributed algorithms for configuration control in multi-robot systems. These algorithms include examples of gradient communication, clustering and dispersion, group motion, and network c...
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This demonstration showcases distributed algorithms for configuration control in multi-robot systems. These algorithms include examples of gradient communication, clustering and dispersion, group motion, and network characterization. The algorithms are demonstrated on a swarm of 15 mobile robots.
A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node...
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A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node in the network drains out of energy. The problem is formulated as a linear programming problem and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal routing scheme that maximizes the network lifetime. The algorithms are illustrated by an example in which an optimal flow is computed for a network of randomly distributed nodes.
Consider a wireless network consisting of a set of wireless nodes and a set of communication links, where each communication link corresponds to a pair of nodes that are within communication radius of each other. Unde...
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ISBN:
(数字)9781728152844
ISBN:
(纸本)9781728141305
Consider a wireless network consisting of a set of wireless nodes and a set of communication links, where each communication link corresponds to a pair of nodes that are within communication radius of each other. Under the primary interference model, two communication links cannot be active at the same time if they are incident to a common node. This model of interference arises in Bluetooth networks, where transmissions between a master node and slave nodes in a piconet are scheduled by time-division duplexing, and in CDMA systems when each node is equipped with a single transceiver. Each communication link has a certain minimum bandwidth quality-of-service requirement. The admission control problem is to determine whether the network has sufficient resources to satisfy the bandwidth requirements. In this work, distributed algorithms are proposed for this admission control problem, and performance guarantees of these distributed algorithms are given. If each node has knowledge of a certain global parameter, then a distributed algorithm for flow admission control is given which has the same performance as an optimal, centralized algorithm, i.e. the distributed algorithm gives a condition that is both necessary and sufficient for a set of flow rates to be feasible.
The current framework of network utility maximization for distributed rate allocation assumes fixed channel code rates. However, by adapting the physical layer channel coding, different rate-reliability tradeoffs can ...
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ISBN:
(纸本)0780391500
The current framework of network utility maximization for distributed rate allocation assumes fixed channel code rates. However, by adapting the physical layer channel coding, different rate-reliability tradeoffs can be achieved on each link and for each end user. Consider a network where each user has a utility function that depends on both signal quality and data rate, and each link may provide a 'fatter' ('thinner') information 'pipe' by allowing a higher (lower) decoding error probability. We propose two distributed, pricing-based algorithms to attain optimal rate-reliability tradeoff, with an interpretation that each user provides its willingness to pay for reliability to the network and the network feeds back congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which is proved to be globally optimal for codes with sufficiently large codeword lengths and user utilities with sufficiently negative curvatures
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