Distance-based Cluster Head Election for Mobile Sensing

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to- peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically elect a cluster head so that (1) different energy saving policies can be implemented and (2) battery lifetime is increased given an energy efficiency policy, in a fair way. We demonstrate that the battery usage can be made more fair by reducing the difference in battery life-time of mobiles by up to 66%

Protocol for Energy Efficient Cluster Head Election for Collaborative Cluster Head Elections.

In wireless sensor networks (WSNs) , energy is a major constrain.Energy efficient network protocols is required to maintain reliable sensing of the sensing field . WSNs that use LEACH protocol, have node in a cluster periodically take trials to become cluster - head,such that the nodes in the cluster becomes cluster - head evenly. Nodes with greater transmission distance to the base station in the cluster dies out faster because it performs more work in transmitting data when it is cluster-head. The distance-based algorithm presented in this project, ensures that work is allocated to the nodes in the cluster more evenly ,thus, all areas of the sensor field is sensed more reliably

Quantized Nonnegative Matrix Factorization

Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF's (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in an extended NMF iteration, namely QNMF. This approach comes in two forms: QNMF with 1) quasi-fixed and 2) adaptive quantization levels. Quantized NMF considers element-wise quantization constraints in the learning algorithm to eliminate defects due to post factorization quantization. We demonstrate significant reduction in the cardinality of the factor signal values set for comparable Signal-to-Noise-Ratios in a matrix decomposition task.QC 20160310EOLA