Modular robots are composed of many independent connected modules which are able to achieve common goals through communications. Many distributed algorithms have better performance if the modules that have to communic...
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Modular robots are composed of many independent connected modules which are able to achieve common goals through communications. Many distributed algorithms have better performance if the modules that have to communicate with all the others, are placed at the center of the system. In this paper, we propose ABC-Center, an iterative algorithm for electing an approximate-center module in modular robots. ABC-Center uses O(1) space per module and O(kd) time, where k is the number of iterations required to terminate and d the diameter of the system. We evaluated our algorithm both on hardware modular robots and in a simulator for large ensemble of robots. The average expected eccentricity of the module elected by ABC-Center is less than 1.25 blocks off for random systems composed of up to 1000 modules. Furthermore, experiments show that our algorithm terminates after a few iterations. Hence, ABC-Center is scalable and adapted to modular robots with low memory resources.
This paper considers the nonconvex and globally coupled problem of joint antenna beamforming and transmit power control, in order to maximize the network-wide utility as a function of attained SIRs. Using a spillage-l...
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ISBN:
(纸本)9781424413973;1424413974
This paper considers the nonconvex and globally coupled problem of joint antenna beamforming and transmit power control, in order to maximize the network-wide utility as a function of attained SIRs. Using a spillage-load characterization for power control [13], we assign utility as a function of attained SIRs and formulate the joint optimization as a utility maximization problem. Despite the highly coupled structure of the problem, we propose an efficient distributed algorithm that is proved to be convergent in general. Despite nonconvexity in the joint optimization, we prove global optimality in the two user case. We find in simulations the algorithm always converges to the global optimal allocation, and the Pareto-optimal tradeoff between power and antenna beamforming in maximizing network utility is illustrated.
Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation...
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ISBN:
(纸本)9781424458363
Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distributed algorithms. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of time- reversible Markov chains. Certain carefully designed Markov chains among this class yield distributed algorithms that solve the log-sum-exp approximated combinatorial network optimization problem. By three case studies, we illustrate that Markov approximation technique not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in various domains with provable performance. We believe the Markov approximation framework will find applications in many network optimization problems, and this paper serves as a call for participation.
This work deals with trajectory optimization for a network of robotic sensors sampling a spatio-temporal random field. We examine the problem of minimizing over the space of network trajectories the maximum predictive...
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ISBN:
(纸本)9781424477456
This work deals with trajectory optimization for a network of robotic sensors sampling a spatio-temporal random field. We examine the problem of minimizing over the space of network trajectories the maximum predictive variance of the estimator. This is a high-dimensional, multi-modal, nonsmooth optimization problem, known to be NP-hard even for static fields and discrete design spaces. Under an asymptotic regime of near-independence between distinct sample locations, we show that the solutions to a novel generalized disk-covering problem are solutions to the optimal sampling problem. This result transforms the search for the optimal trajectories into a geometric optimization problem. Constrained versions of the latter are also of interest as they can accommodate trajectories that satisfy a maximum velocity restriction on the robots. We characterize the solution for the unconstrained and constrained versions of the problem as generalized multicircumcenter trajectories, and provide distributed algorithms to find them.
In an ad hoc network, we cannot assume a trusted certificate authority and a centralized repository that are used in ordinary public-key infrastructure (PKI). Hence a PKI system of the Web-of-trust type in which each ...
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In an ad hoc network, we cannot assume a trusted certificate authority and a centralized repository that are used in ordinary public-key infrastructure (PKI). Hence a PKI system of the Web-of-trust type in which each node can issue certificates to others in a self-organizing manner has been studied. Although this system is useful for ad hoc networks whose topology can change, it has the problem that for authentication a node needs to find a certificate-chain to the destination node. In this paper, we formally model a web-of-trust-type PKI system, define the certificate-chain discovery problem, and propose a new distributed algorithm and its modifications that solve the problem. Furthermore, we propose a measure of communication cost, and according to the measure, we compare our algorithm with an existing method.
A wireless sensor network performing surveillance in time-critical missions involving event or target tracking demands accurate ground information be delivered within a delay guarantee. Present methods solve this by u...
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A wireless sensor network performing surveillance in time-critical missions involving event or target tracking demands accurate ground information be delivered within a delay guarantee. Present methods solve this by using in-network fusion across all packets to reduce network load in the hope of achieving the delay guarantee. In this paper, we aim to maximize data quality from sensor fusion, while still respecting delay guarantees. The proposed method makes admission control and routing decisions using a fully distributed algorithm based on constrained Markov Decision Processes (MDPs). Cooperation is enforced through well-defined rewards and leading nodes. Assessment of data quality is derived from likelihood ratio, which is a commonly used metric in sensor fusion. We study the performance of the proposed algorithm through extensive simulations, and show that it can achieve soft delay guarantees and good data quality compared to other schemes.
A distributed application may comprise a range of processors, from two to thousands. For the processors of an application comprising many different machines to work together, they must often coordinate. Coordination m...
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ISBN:
(纸本)9781605586069
A distributed application may comprise a range of processors, from two to thousands. For the processors of an application comprising many different machines to work together, they must often coordinate. Coordination may be as simple as agreeing upon a basic configuration, e.g., a list of server addresses and ports for example. In the context of distributed systems, even agreeing upon a basic configuration can be complicated: servers may be down during configuration changes, servers may need to adapt quickly to dynamic configuration changes, and during changes servers may have conflicting configurations. Most distributed applications also require more sophisticated coordination primitives such as leader election, group membership, and *** Yahoo!, we noticed that many distributed applications were re-implementing coordination primitives in their distributed applications. In theory, this is completely reasonable: coordination protocols are well known, and usually the coordination logic is intermingled with the application logic. In practice, the story is much different; these protocols have sometimes subtle requirements that can easily be overlooked when implementing them. Further, an application developer is usually much more interested in working on application logic than the coordination protocol that the logic depends on. We found many cases of applications whose coordination primitives were buggy, a single point of failure, poorly performing, or oversimplified; in some cases the applications suffered from all of the *** started ZooKeeper as a system that could address all these problems in a general way so that all of our applications could use it for coordination and application developers could focus on developing their applications. By providing a general system that all applications could use, we could devote the time to making it robust, fault-tolerant, and with good enough performance to be used extensively by applications. We also needed to b
This work addresses the spectrum access problem in cognitive radio relay networks. In our setup, primary users allow secondary users access to the channel (spectrum) as long as they agree to relay primary users' d...
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ISBN:
(纸本)9781467303217
This work addresses the spectrum access problem in cognitive radio relay networks. In our setup, primary users allow secondary users access to the channel (spectrum) as long as they agree to relay primary users' data in addition to their own. We desire to maximize the network usage by determining the best configuration of matching between primary users and secondary users. This problem can be formulated in a form similar to maximum weighted matching. Given this formulation, we develop an algorithm for this problem using affinity propagation that is fully distributed. We test its performance and demonstrate convergence.
A new distributed algorithm, called Crystal-Lattice Permutation (CLP) algorithm, is devised to approach the coverage problem in wireless sensor networks. All nodes are assumed as mobile and run the same CLP algorithm ...
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A new distributed algorithm, called Crystal-Lattice Permutation (CLP) algorithm, is devised to approach the coverage problem in wireless sensor networks. All nodes are assumed as mobile and run the same CLP algorithm in . An initial seed triggers six neighboring nodes to move to hexagon permutation positions, which is like the Crystal-Lattice formation process in nature. These chosen nodes then become new seeds to trigger other neighboring nodes. By such a progressive process, with initial and boundary conditions, the coverage is achieved. Comparing to the Virtual Force algorithm (VFA), the CLP algorithm needs fewer nodes and takes shorter average moving distance to achieve 100% coverage in random deployment. In simulation, the CLP algorithm deployed at least 60 nodes to achieve the 100% coverage, while the VFA algorithm required more than 100 nodes, under the same assumptions. The average moving distance for each CLP node was less than a half of the VFA node. In addition, as there are n mobile nodes, the number of CLP messages is proportional to n, while VFA is proportional to n2. The CLP algorithm is robust in fault tolerance when some nodes are error in distribution.
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