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In wireless sensor networks, sensor nodes are often deployed intensively within the sensing area in order to achieve effective monitoring, resulting in a high degree of correlation between them. There is a certain variation rule between node acquisition data and time. The current time correlation will result in an abundance of redundant data within the sensing area, so eliminating data redundancy...
The derivative based prediction (DBP) is an algorithm for reducing the number of messages needed to transfer the data samples from a wireless sensor node to a sink, in real-time. The algorithm computes a linear fit over the time series and sends only the updates of the linear model to the sink, when needed. This paper presents two extensions of the original algorithm that further decrease the number...
A considerable amount of energy efficient routing algorithms have been proposed to save energy and prolong network lifetime. Those algorithms mainly focus on forwarding packets along the minimum energy path to the sink to merely minimize energy consumption, which causes an unbalanced distribution of residual energy among sensor nodes, and eventually results in a network partition. In this paper, we...
Massive historical data are stored in chlorine gas monitoring network. So that prediction algorithm of data mining is used to dig historical data not only can make the redundant data reused, but also can forecast the network trend and improve the network early warning model. The chlorine gas monitoring wireless sensor network based on ZigBee was designed in this paper. Then Fletcher-Reeves algorithm...
The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique...
In Wireless Sensor Networks (WSN), Node Localization is of great importance for location aware services. In this paper we propose the use of Time of Arrival (TOA) information with two popular machine learning algorithms M5 tree Model (M5P) and Sequential Minimal Optimization for Regression (SMOreg) for more accurate node localization in WSN. In this paper we also applied the same node localization...
The autonomic management of large-scale distributed systems now allows performance improvement, availability, and security, while simultaneously reducing the effort and skills required of system administrators. One way that systems can support these abilities is by relying on a continuous monitoring service to keep track of the states of the targeted systems. However, it is challenging to achieve...
We describe a Bayesian formalism for the intelligent selection of observations from sensor networks that may intermittently undergo faults or changepoints. Such active data selection is performed with the goal of taking as few observations as necessary in order to maintain a reasonable level of uncertainty about the variables of interest. The presence of faults/changepoints is not always obvious and...
In wireless sensor networks (WSNs), sink nodes are the bottleneck of network. As sensor network own its characteristics, the traditional congestion control strategy can't be used directly any longer. Most of the existing congestion control strategies and algorithms are not fully considered RTT. At the same time as the actual sensor network operating in the nonlinear, time delays and time-varying parameters...
An energy-efficient data gathering protocol called E2DGP that takes advantage of spatial and temporal correlation of sampling data for WSNs is proposed in this paper. E2DGP includes a clustering method of balancing energy consumption, a data prediction transmission strategy and an energy-aware multihop routing algorithm. In clustering process phase, the initial probability of node for cluster head...
Energy efficient data collection protocols are required in order to better manage the limited energy, memory and processing capabilities of sensor networks. In applications where data are collected in real time, efficient management of sensor radio assumes critical significance because communication is energy intensive. Moreover, specialised sensors exist which consume even more energy than radio...
In this paper, a novel strategy for data transmission that is based on prediction revision dynamic adjustment data gathering algorithm (PRDA) is proposed in WSNs. The key idea of the PRDA is to separate the data prediction and model computing, and the autoregressive process model is employed for prediction revision algorithm. The model computing of PRDA is conducted by sink node firstly according...
For target tracking in Interference Environments of cognitive radar problem, Extended Karman, Particle filter algorithms etc. are generally used to be regarded as usual solutions to state estimation. Many techniques have been developed to improve performance of target tracking. In this paper, we set the structure and key features of target's tracking design for cognitive radar, and newly propose cognitive...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
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