Using SDR technology, the distributed algorithm RMA is used by sensor nodes to reconfigure their radio according to some predefined radio-modes, such that the resulting topology is connected to the sink. This mechanis...
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Using SDR technology, the distributed algorithm RMA is used by sensor nodes to reconfigure their radio according to some predefined radio-modes, such that the resulting topology is connected to the sink. This mechanism reduces interference and increases data delivery rate. In this paper we extend RMA by adding a low overhead mechanism to be used in the presence of a primary user. Sensor nodes operating on the same frequency as the primary user will reassign their radio-mode such that the resulting topology does not interfere with the primary user. We analyze the performance of our distributed algorithm using ns-3 simulations.
Groupware systems have been made possible by developing algorithms and architectures which support collaborative tasks. The main rules of these algorithms are to maintain a global coherence state of the cooperative sy...
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Groupware systems have been made possible by developing algorithms and architectures which support collaborative tasks. The main rules of these algorithms are to maintain a global coherence state of the cooperative system and to control the information flow amongst the co-users. By architecture the authors mean the way the cooperative system is organized in order to enable the cooperative algorithm to work. The distribution of the physical processes and files over the different machines where the co-authors are located, as well as the way the communication is enabled, is discussed.< >
We considered a dense interference network with a large number (K rarr infin) of transmitter-receiver pairs. Each transmitter is endowed with a finite buffer and accepts packets from an arrival process. Each transmitt...
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We considered a dense interference network with a large number (K rarr infin) of transmitter-receiver pairs. Each transmitter is endowed with a finite buffer and accepts packets from an arrival process. Each transmitter-receiver link is a fading vector channel with N diversity paths whose statistics are described by a Markov chain. We investigate distributed algorithms for joint admission control, rate and power allocation aiming at maximizing the individual throughput defined as the average information rate successfully received. The decisions are based on the statistical knowledge of the channel and buffer states of the other communication pairs and on the exact knowledge of their own channel and buffer states. In the case of a finite number of communication pairs this problem is computationally extremely intensive with an exponential complexity in the number of users. Assuming that K,N rarr infin with constant ratio the algorithm complexity becomes substantially independent of the number of active communications and grows with the groups of users having distinct asymptotic channel statistics. The cross-layer design is investigated for different kind of decoders at the receiver. The benefits of a cross layer approach compared to a resource allocation ignoring the states of the queues are assessed. The performance loss due to the use of policies designed for asymptotic conditions and applied to networks with a finite number of active communications is studied.
We discuss an old distributed algorithm for reaching consensus that has received a fair amount of recent attention. In this algorithm, a number of agents exchange their values asynchronously and form weighted averages...
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We discuss an old distributed algorithm for reaching consensus that has received a fair amount of recent attention. In this algorithm, a number of agents exchange their values asynchronously and form weighted averages with (possibly outdated) values possessed by their neighbors. We overview existing convergence results, and establish some new ones, for the case of unbounded intercommunication intervals.
In sensor networks many efforts have been made on barrier coverage. Most of them rely on the assumption that sensors are randomly or manually deployed around the region of interest. It is obvious that the random deplo...
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In sensor networks many efforts have been made on barrier coverage. Most of them rely on the assumption that sensors are randomly or manually deployed around the region of interest. It is obvious that the random deployment wastes many redundant sensors without contribution on the barrier formation. Moreover, in most real scenarios, it is difficult to deploy sensors manually due to the region usually in large scale or in danger. Hence, this paper studies the problem that using mobile sensors to form barrier surrounding the region automatically. The fundamental objective is to take full advantage of the limited number of mobile sensors to form the barrier coverage with the highest detection capability. The challenge is that the sensors only have local information. A fully distributed algorithm based on virtual force and convex analysis is developed for the objective to relocate the sensors from the original positions to uniformly distribute on the convex hull of the region. Simulation results verify the validity of our proposed cooperative scheme.
In WSN-based surveillance systems power conservation is an important issue but one must also ensure that the application's objectives are met. In this paper, we jointly address the problem of coverage, disambigati...
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In WSN-based surveillance systems power conservation is an important issue but one must also ensure that the application's objectives are met. In this paper, we jointly address the problem of coverage, disambigation and adaptive scheduling of sensor node's activity to extend the network lifetime while fulfilling a critical surveillance application needs. We first present a distributed algorithm that ensures both coverage of the deployment area and network connectivity by providing multiple cover sets to manage Field of View redundancies. The availability of multiple cover sets leads to a simple but efficient disambigation algorithm. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for adaptive scheduling. Simulation results are presented to validate the performance of the proposed approach.
In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the us...
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In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations. This leads to a distributed algorithm in which nodes exchange updates only with neighboring nodes. We show that the widely studied consensus algorithm is indeed only a very specific case of our more general formulation. Finally, we study the convergence rate and propose some approaches to maximize it.
Support of traffic with QoS requirements in wireless scenarios is a challenging topic and methodologies for supporting different traffic priorities have recently emerged. Unfortunately in wireless scenarios, it is wel...
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ISBN:
(纸本)9781424425167
Support of traffic with QoS requirements in wireless scenarios is a challenging topic and methodologies for supporting different traffic priorities have recently emerged. Unfortunately in wireless scenarios, it is well known that contention problems are met. The latter can be modeled as a game. Accordingly, in this paper we present a game-theoretic model of prioritized data transmission in CSMA/CA networks where nodes are considered as potential cheaters which should be punished through a jamming mechanism when misbehaving and trying to monopolize wireless resources. Then, we also describe the distributed algorithm to be performed by network nodes to implement the model and assess the validity of both the model and the algorithm through simulations.
This study presents the ant colony system (ACS) algorithms for optimization of power systems planning. The developed ACS algorithms formulate complex problems as combinatorial optimization problems. They are distribut...
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This study presents the ant colony system (ACS) algorithms for optimization of power systems planning. The developed ACS algorithms formulate complex problems as combinatorial optimization problems. They are distributed algorithms composed by a set of cooperating artificial agents, called ants, which cooperate to find an optimum solution of the combinatorial problems. A pheromone matrix that plays the role of global memory provides the cooperation between ants. The study consists of mapping the solution space, expressed by an objective function of the combinatorial problems on the space of control variables, ant system (AS)-graph, where ants walk. In this study an ACS algorithm is applied to the constrained load flow (CLF) problem on IEEE 14-bus test system and 136 bus system. The results are compared with those given by the probabilistic CLF and the reinforcement learning (RL) methods, demonstrating the superiority and flexibility of the ACS algorithm. Moreover, ACS algorithm is applied to the reactive power control problem on the IEEE 14-bus test system in order to minimize the real power losses subject to operating constraints over the whole planning period. Finally, the application of ACS algorithm for active/reactive operational planning of power systems on IEEE 30-bus test system is presented and results are compared to those given by simulated annealing (SA), exhibiting superior performance
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