An Effective Collaborative Mobile Weighted Clustering Schemes for Energy Balancing in Wireless Sensor Networks

Self Cite

To implement minimum power consumption of the link, cluster heads adopt the multi-hop manner for inter-cluster communication so as to forward the aggregation data to the relay nodes. This paper proposes a collaborative data aggregation in emerging sensor networks using a bio-level Voronoi diagram, which is an energy-efficient data aggregation protocol that integrates topology control, Multiple Access Control (MAC) and routing. The sensor nodes situated in the lower level of the diagram are responsible for listening and gathering data, and should be organized by optimal clustering node. In the inter-cluster communication stage, a particle swarm optimization algorithm is addressed to seek optimal transmission path which could simultaneously achieve the minimization of the maximum next hop distance between two nodes in the routing path and the minimization of the maximum hop count, so the minimization of whole network energy consumption is realized. The results of theoretical analysis and simulation results show that energy efficiency and synchronization accuracy of the proposed algorithm can be much better than with traditional routing protocols, and the energy consumption of nodes in the whole network can be more balanced.

Section: Simulation Resultsmentioning

confidence: 99%

CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams

Tang

Yang

2016

Self Cite

“…The controlled process of a discrete Kalman Filter can be represented as two linear stochastic difference equations [45,46,47]. x k is the system state at the time k , which can be estimated by the following Equation (8):

x_{k} = A_{k} x_{k - 1} + B u_{k} + w_{k - 1}

where u k is the system control variable, the variables A k and B k represent the parameters of the system state model, w k is the process noise.…”

Section: Discrete Kalman Filter Modelmentioning

confidence: 99%

“…We have combined our method with power saving schemes for smart home sensor networks. We compare WHRA-KF and Collaborative Weighted Clustering Algorithm (CWCA) [47] in sensor networks.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

Chen

Xie

Shu

et al. 2016

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

“…Clustering is an important topic in machine learning, which aims to discover similar data and group them into clusters. Various clustering algorithms have been proposed and widely used in different areas such as sensor networks [ 1 , 2 , 3 , 4 ], image processing [ 5 , 6 , 7 ], data mining [ 8 , 9 , 10 ], text information processing [ 11 , 12 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

Liang

Han

2017

Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC). Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.