The Experts below are selected from a list of 240 Experts worldwide ranked by ideXlab platform
Karl Henrik Johansson - One of the best experts on this subject based on the ideXlab platform.
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CDC - Randomized gossiping with unreliable communication: Dependent or Independent Node updates
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Mikael Johansson, Karl Henrik JohanssonAbstract:This paper studies an asynchronous randomized gossip algorithm under unreliable communication. At each instance, two Nodes are selected to meet with a given probability. When Nodes meet, two unreliable communication links are established with communication in each direction succeeding with a time-varying probability. It is shown that two particularly interesting cases arise when these communication processes are either perfectly dependent or Independent. Necessary and sufficient conditions on the success probability sequence are proposed to ensure almost sure consensus or ?-consensus. Weak connectivity is required when the communication is perfectly dependent, while double connectivity is required when the communication is Independent. Moreover, it is proven that with odd number of Nodes, average preserving turns from almost forever (with probability one for all initial conditions) for perfectly dependent communication, to almost never (with probability zero for almost all initial conditions) for the Independent case. This average preserving property does not hold true for general number of Nodes. These results indicate the fundamental role the Node interactions have in randomized gossip algorithms.
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Randomized gossiping with unreliable communication: Dependent or Independent Node updates
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Mikael Johansson, Karl Henrik JohanssonAbstract:This paper studies an asynchronous randomized gossip algorithm under unreliable communication. At each instance, two Nodes are selected to meet with a given probability. When Nodes meet, two unreliable communication links are established with communication in each direction succeeding with a time-varying probability. It is shown that two particularly interesting cases arise when these communication processes are either perfectly dependent or Independent. Necessary and sufficient conditions on the success probability sequence are proposed to ensure almost sure consensus or ε-consensus. Weak connectivity is required when the communication is perfectly dependent, while double connectivity is required when the communication is Independent. Moreover, it is proven that with odd number of Nodes, average preserving turns from almost forever (with probability one for all initial conditions) for perfectly dependent communication, to almost never (with probability zero for almost all initial conditions) for the Independent case. This average preserving property does not hold true for general number of Nodes. These results indicate the fundamental role the Node interactions have in randomized gossip algorithms.
Mikael Johansson - One of the best experts on this subject based on the ideXlab platform.
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CDC - Randomized gossiping with unreliable communication: Dependent or Independent Node updates
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Mikael Johansson, Karl Henrik JohanssonAbstract:This paper studies an asynchronous randomized gossip algorithm under unreliable communication. At each instance, two Nodes are selected to meet with a given probability. When Nodes meet, two unreliable communication links are established with communication in each direction succeeding with a time-varying probability. It is shown that two particularly interesting cases arise when these communication processes are either perfectly dependent or Independent. Necessary and sufficient conditions on the success probability sequence are proposed to ensure almost sure consensus or ?-consensus. Weak connectivity is required when the communication is perfectly dependent, while double connectivity is required when the communication is Independent. Moreover, it is proven that with odd number of Nodes, average preserving turns from almost forever (with probability one for all initial conditions) for perfectly dependent communication, to almost never (with probability zero for almost all initial conditions) for the Independent case. This average preserving property does not hold true for general number of Nodes. These results indicate the fundamental role the Node interactions have in randomized gossip algorithms.
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Randomized gossiping with unreliable communication: Dependent or Independent Node updates
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Mikael Johansson, Karl Henrik JohanssonAbstract:This paper studies an asynchronous randomized gossip algorithm under unreliable communication. At each instance, two Nodes are selected to meet with a given probability. When Nodes meet, two unreliable communication links are established with communication in each direction succeeding with a time-varying probability. It is shown that two particularly interesting cases arise when these communication processes are either perfectly dependent or Independent. Necessary and sufficient conditions on the success probability sequence are proposed to ensure almost sure consensus or ε-consensus. Weak connectivity is required when the communication is perfectly dependent, while double connectivity is required when the communication is Independent. Moreover, it is proven that with odd number of Nodes, average preserving turns from almost forever (with probability one for all initial conditions) for perfectly dependent communication, to almost never (with probability zero for almost all initial conditions) for the Independent case. This average preserving property does not hold true for general number of Nodes. These results indicate the fundamental role the Node interactions have in randomized gossip algorithms.
Kofi A. Laing - One of the best experts on this subject based on the ideXlab platform.
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Compact roundtrip routing with topology-Independent Node names
Journal of Computer and System Sciences, 2008Co-Authors: Marta Arias, Lenore J. Cowen, Kofi A. LaingAbstract:Consider a strongly connected directed weighted network with n Nodes. This paper presents compact roundtrip routing schemes with [email protected]?(n) sized local tables and stretch 6 for any strongly connected directed network with arbitrary edge weights. A scheme with local tables of size [email protected]?(@e^-^1n^2^/^k) and stretch min((2^k^/^2-1)([email protected]),8k^2+4k-4), for any @e>0 is also presented in the case where edge weights are restricted to be polynomially-sized. Both results are for the topology-Independent Node-name model. These are the first topology-Independent results that apply to routing in directed networks.
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PODC - Compact roundtrip routing with topology-Independent Node names
Proceedings of the twenty-second annual symposium on Principles of distributed computing - PODC '03, 2003Co-Authors: Marta Arias, Lenore J. Cowen, Kofi A. LaingAbstract:This paper presents compact roundtrip routing schemes with local tables of size O(√n) and stretch 6 for any directed network with arbitrary edge weights; and with local tables of size O(√−1n2/k) and stretch min((2k/2 −1)(k + √), 16k 2+ 8 k − 8), for any directed network with polynomially-sized edges, both in the topology-Independent Node-name model. These are the first topology-Independent results that apply to routing in directed networks.
Shahrokh Valaee - One of the best experts on this subject based on the ideXlab platform.
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Strategic Sensing in Vehicular Networks Using Known Mobility Information
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Waleed Alasmary, Hamed Sadeghi, Shahrokh ValaeeAbstract:In this paper, we study the problem of sensing targets in the context of vehicular networks. First, we define targets to be the vehicles moving on the road and sensors to be the roadside cameras. Then, we study the effect of predicted mobility on reducing the number of times each camera is activated in order to guarantee the coverage of targets. We formulate the sensing problem as an integer linear program using an opportunistic scheduler. Afterward, we extend the formulation and propose a novel strategic scheduler for coverage, which utilizes the predicted mobility information. We then extend this method to a fully distributed version and propose an approximation algorithm by exchanging messages among the sensors. Using a Markovian and a car-following availability models, we show by simulations that the number of activated sensors is significantly reduced by utilizing predicted mobility information. After that, we analyze both schedulers to quantify the gain of utilizing mobility information in sensing. We adopt an Independent Node mobility model due to its tractability. The analysis is composed of two main components; calculation of mobility gain in terms of sensing cost and probability of feasibility. Our analysis and simulations demonstrate the gain of mobility in sensing targets in terms of higher probability of feasibility and lower sensing cost.
Alan Willsky - One of the best experts on this subject based on the ideXlab platform.
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Limit laws for random spatial graphical models
2010 IEEE International Symposium on Information Theory, 2010Co-Authors: Animashree Anandkumar, Joseph Yukich, Alan WillskyAbstract:We consider spatial graphical models on random Euclidean points, applicable for data in sensor and social networks. We establish limit laws for general functions of the graphical model such as the mean value, the entropy rate etc. as the number of Nodes goes to infinity under certain conditions. These conditions require the corresponding Gibbs measure to be spatially mixing and for the random graph of the model to satisfy a certain localization property known as stabilization. Graphs such the k nearest neighbor graph and the geometric disc graph belong to the class of stabilizing graphs. Intuitively, these conditions require the data at each Node not to have strong dependence on data and positions of Nodes far away. Finally, it is shown that spatial mixing of the Gibbs measure on a random graph holds when a suitably defined degree-dependent (but otherwise Independent) Node percolation does not have a giant component.
