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Analyzed the energy consumption disciplinarian of the nodes in WSN, the node's energy attenuation forecast model (EAFM) can be established. A difference-threshold reporting mechanism (DTRM) is used to report the residual energy of nodes. The energy collection mechanism based on EAFM and DTRM can reduce energy data reporting times significantly, improve the efficient of energy data collection, save...
Shrinking size and increasing deployment density of wireless sensor nodes implies to smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization. MAC protocols play a vital role in energy consumption of sensor node as it controls the radio activities. Customized or open source simulators play an important role to measure the performance effectiveness...
In this paper, the regression framework is proposed to hierarchically organize the three regression procedures, that is, local data modelling, cluster data modelling and global data modelling, for the energy-efficient data acquisition in wireless sensor networks (WSNs). There are two advantages arising from our method. First, there is no need for any transmissions of the measured data in the modelling...
This paper presents a Jacobi iterative based computational paradigm for solving the data regression in wireless sensor networks (WSNs). The in-network computational scheme is proposed to construct a mixture regression model through the cluster-based Jacobi distributed iteration, where the intersections among mixture structure of regression model are decoupled through a new cluster-based message passing...
With the emerging practical application and rapid advancement of WSN in EHS, this paper addresses the particularly important problem on healthcare sensor information fusion. On light of the common ground among WSN, MAS and RST, some hybrid and multi-agent approaches are discussed and implementation details of the method are given, and as can be seen, the proposed solution MADHSDN, which results in...
To overcome the disadvantage of the imperfect and uncertain data and redundancy node, reduce energy efficiency of communications and data processing, an optimization model based on agent distributed computation is proposed. In this model, vague set theory is used to optimize and reduce data. Furthermore, it is applied to intelligent information processing of wireless sensor networks (WSN) as clusters...
The unplanned and random deployment of a Wireless Sensor Network (WSN) may impose a high node density on a specific region. This concentration can be exploited by density control mechanisms to increase network lifetime, by deactivating temporarily redundant sensor nodes. Previous approaches for density control in WSN focus in guaranteeing full sensing coverage of monitoring area. This work presents...
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