Wireless sensor network

About: Wireless sensor network is a research topic. Over the lifetime, 142021 publications have been published within this topic receiving 2448622 citations. The topic is also known as: WSN & sensor grid.

Cluster-Based Energy-Efficient Data Collection in Wireless Sensor Networks Utilizing Compressive Sensing

Abstract: In this paper, an integration of compressive sensing (CS) and clustering in wireless sensor networks (WSNs) is proposed to significantly reduce the energy consumption related to data collection in such networks. Both compressive sensing (CS) and clustering have been proved to be efficient ways to reduce the energy consumptions in WSNs, however, there is little study about the integration of them for further gains. The idea is to partition a WSN into clusters, in which each cluster head collects the sensor readings within its cluster and forms CS measurements to be forwarded to the base station. The spatial correlation of the readings in a WSN results in an inherent sparsity of data in a proper basis such as discrete cosine transform (DCT) or Wavelet. This sparsity can then facilitate the application of the CS in data collection in WSNs. This way, we only need to forward l N CS measurements from N sensor nodes. An important issue that needs to be considered for applying CS in the data collection problem is the underlying routing mechanism. Some related studies employ minimum spanning tree, random walk, or gossiping as the routing mechanism. However, we propose applying CS on top of a clustering algorithm to reduce the energy consumption. Under this novel framework, we study different clustering techniques and the properties of the block diagonal measurement matrix that is formed based on the clustering algorithm. We further formulate and analyze the total power consumption, based on that we can obtain the optimal number of clusters for reaching the minimum power consumption.

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.

A middleware platform to support river monitoring using wireless sensor networks

Abstract: Flooding is a critical global problem, which is growing more severe due to the effects of climate change. This problem is particularly acute in the state of Sao Paulo, Brazil, where flooding during the rainy season incurs significant financial and human costs. Another critical problem associated with flooding is the high level of pollution present in urban rivers. Efforts to address these problems focus upon three key research areas: river monitoring, modelling of river conditions and incident response. This paper introduces a rich next-generation middleware platform designed to support wireless sensor network based environmental monitoring along with a supporting hardware platform. This system has been deployed and evaluated in a real-world river monitoring scenario in the city of Sao Carlos, Brazil.

DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks

Abstract: Current approaches to k nearest neighbor (KNN) search in mobile sensor networks require certain kind of indexing support This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use In this paper, we propose a maintenance-free, itinerary-based approach called density-aware itinerary KNN query processing (DIKNN) The DIKNN divides the search area into multiple cone-shape areas centered at the query point It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel The design of the DIKNN scheme also takes into account challenging issues such as the the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases It outperforms the second runner with up to 50% saving in energy consumption and up to 40% reduction in query response time, while rendering the same level of query result accuracy