A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.

Increasing spatial coverage in rough terrain and vegetated areas using InSAR optimized pixel selection:application to Tohoku,Japan

One of the major limitations of using Interferometric Synthetic Aperture Radar(InSAR)in time series analysis is the low-phase coherence associated with rough terrain and vegetated areas,which results in limited spatial coverage in such regions.Permanent scatterers technique was introduced to overcome this limitation using time-series analysis.However,identifying major scatterers within a pixel requires the single-looked pixels oversampling which can be a demanding process especially with large interferometric stacks and vast study areas.Therefore,using multilooked temporal coherent pixels was proposed to increase processing efficiency and coverage by utilizing distributed targets,but this technique may exclude pixels with reliable phase returns because of their temporal varying neighboring pixels.In this paper,we propose a technique to identify multilooked temporal stable pixels with reliable phase returns independent of their neighboring pixels.We conduct a simulation analysis to relate the spatial coherence of a pixel with its expected temporal correlation in the time series analysis module.We found that a liberal temporal correlation threshold of 0.53 in multilooked pixels stack is equivalent to a spatial coherence threshold of 0.2 when using number of looks of 9,which is considered acceptable in temporal coherent pixels,in terms of phase standard deviation.Applying these findings to study the 2011 Tohoku earthquake in the northeastern part of Japan resulted in increasing the number of usable pixels and spatial coverage index by nearly 50.4%and 36.8%,respectively,compared to the temporal coherent pixels.Furthermore,we propose an approach to integrate GPS observations with InSAR time series analysis,which resulted in deformation maps of the megathrust 2011 Tohoku earthquake with mean RMSE of 11.4 mm and a correlation of 98%in comparison to GPS observations.