Wireless Sensor Network(WSN) is a major and very interesting technology employed in different applications like monitoring the inaccessible conditions in a specific area. Each sensor node consists of a battery, transm...
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Wireless Sensor Network(WSN) is a major and very interesting technology employed in different applications like monitoring the inaccessible conditions in a specific area. Each sensor node consists of a battery, transmitter, receiver and a processor. Replacing or recharging the battery is not possible every time. Therefore maximizing the network lifetime by decreasing the energy consumption of the entire sensor nodes and load balancing are the main challenges in the research of the routing protocols in WSNs. In this paper, Energy efficient clustering for wireless sensor networks using Firefly algorithm is implemented at the base station. A new cost function has been defined to minimize the intra-cluster distance to optimize the energy consumption of the network. The improvement in performance is presented in comparison with the previous protocols LEACH(Low Energy Adaptive Clustering Hierarchy) and PSO-C(centralized Particle Swarm Optimization).
We consider the fairness problem of outage probability balancing based on statistical channel state information (CSI) in closed-access femtocell networks where multiple sub-channels are shared among macro and femto us...
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ISBN:
(纸本)9781479935130
We consider the fairness problem of outage probability balancing based on statistical channel state information (CSI) in closed-access femtocell networks where multiple sub-channels are shared among macro and femto user equipments. We aim to maximize a utility function of outage probabilities for each femto user equipment (FUE) in the network under a macro user equipment (MUE) outage constraint. In light of rate requirement for each FUE, we jointly optimize the power allocation for both FUEs and MUEs as well as the rate allocation for FUE in each sub-channels. Using successive convex approximation, we develop a centralized algorithm to iteratively tackle a series of geometric programming problems. For base-stations connected through back-haul, we further design a distributed algorithm to update optimization variables locally through message passing.
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (...
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ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066322
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to non-cooperative approaches in the strongly-convex setting. More recently, these results have been extended to the pursuit of first-order stationary points in non-convex environments. In this work, we examine in detail the dependence of second-order convergence guarantees on the spectral properties of the combination policy for non-convex multi agent optimization. We establish linear speedup in saddle-point escape time in the number of agents for symmetric combination policies and study the potential for further improvement by employing asymmetric combination weights. The results imply that a linear speedup can be expected in the pursuit of second-order stationary points, which exclude local maxima as well as strict saddle-points and correspond to local or even global minima in many important learning settings.
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