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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Energy Aware, Scalable, K-Hop Based Cluster Formation In ...
Priyanka Chatterjee, Nikhil Agarwal · 2012-09-21 · via cs.DS updates on arXiv.org

The study of Mobile Ad-hoc Network remains attractive due to the desire to achieve better performance and scalability. MANETs are distributed systems consisting of mobile hosts that are connected by multi-hop wireless links. Such systems are self organized and facilitate communication in the network without any centralized administration. MANETs exhibit battery power constraint and suffer scalability issues therefore cluster formation is expensive. This is due to the large number of messages passed during the process of cluster formation. Clustering has evolved as an imperative research domain that enhances system performance such as throughput and delay in Mobile Ad hoc Networks (MANETs) in the presence of both mobility and a large number of mobile terminals.In this thesis, we present a clustering scheme that minimizes message overhead and congestion for cluster formation and maintenance. The algorithm is devised to be independent of the MANET Routing algorithm. Depending upon the context, the clustering algorithm may be implemented in the routing or in higher layers. The dynamic formation of clusters helps reduce data packet overhead, node complexity and power consumption, The simulation has been performed in ns-2. The simulation shows that the number of clusters formed is in proportion with the number of nodes in MANET.