Kriging interpolation based sensor node position management in dynamic environment

Abstract

The use of Wireless Sensors Network (WSN) is growing not only in popularity, but also in scope and application. Poised to shape the way we monitor the environment, wireless sensors has a lot of potentials waiting to be unlocked. Performance of WSN is largely determined by coverage and positioning of the sensor nodes. However, to be accepted as de-facto solution for monitoring different phenomena in our physical world, it is necessary to develop acceptable standards for its configuration and deployment. Motivated by this requirement, this paper investigates Kriging interpolation algorithm as an efficient location management system. With the aid of Kriging interpolation, it will be demonstrated later in this paper how targets of interest can be classified and monitored in a distributed manner for better accuracy. Effectiveness of this method on a large scale was established by applying it to temperature-based sensor network in a dynamic environment. Kriging interpolation sensor management is expected to improve coverage, reduce power consumption and enhance performance of sensor nodes and by extension the network.

6LoPLC for smart grid applications

“…The cohesive power of such subsystems will be harnessed via large-scale integration over the entire electrical power value chain; spanning generation to smart meters. A major beneficiary of such integration are the smart meters deployed to monitor the consumer end of electricity distribution environments and send measured observations to a target of interest; usually a server, these features summarily describe them as sensors [9]. In addition to automatic reading, smart meters will act as sensors, sending estimates of power quality, alarms, and other information useful for price setting, thereby providing better visibility.…”

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations

“…Optimization methods were applied aimed at reducing the number of nodes while also reducing the energy consumption of the entire WSN. Other works reported in [23][24][25][26][27] also present optimal approaches for the spatial distribution of WSN nodes, considering several properties of the soil and irrigation systems, among other variables.…”

Location prediction optimisation in WSNs using Kriging interpolation

“…Moses et al [3] and Ali et al [4] attempt to provide answers to some of these questions through numerical results and quantitative analysis of simulations results, respectively. Despite the benefits of WSNs, the unpredictable nature of the wireless medium coupled with the short communication range sometimes renders their performance inadequate and anticipated level of service unattainable.…”