A Nobel Approach for Entropy Reduction of Wireless Sensor Networks (WSN)

In contrast to RF, optical devices are smaller and consume less power; reflection, diffraction, and scattering from aerosols help distribute signal over large areas; and optical wireless provides freedom from interference and eavesdropping within an opaque enclosure. For a densely deployed Wireless Multimedia Sensor Network (WMSN), an entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. The limitations of limited energy, processing power and bandwidth capabilities of sensors networks become critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. Data aggregation is considered to be an effective technique. Selective use of informative sensors reduces the number of sensors needed to obtain information about the target state and therefore prolongs the system lifetime. In this paper the use of entropy in spectrum sensing is also described. This sensing gives knowledge about the usage of spectrum by primary user and based on that a secondary user can utilize the unused spectrum without interfere the primary user

Near-Optimal Source Placement for Linear Physical Fields

In real-word applications, signal processing is often used to measure and control a physical field by means of sensors and sources, respectively. An aspect that has been often neglected is the optimization of the sources' locations. In this work, we discuss the source placement problem as the dual of the sensor placement problem and propose two polynomial-time algorithms, for scenarios with or without noise. Both algorithms are near-optimal and indicate the possibility to make the control of such physical fields easier, more efficient and stabler to noise

Sensor optimization in smart insoles for post-stroke gait asymmetries using total variation and L1 distances

By deploying pressure sensors on insoles, the forces exerted by the different parts of the foot when performing tasks standing up can be captured. The number and location of sensors to use are important factors in order to enhance the accuracy of parameters used in assessment while minimizing the cost of the device by reducing the number of deployed sensors. Selecting the best locations and the required number of sensors depends on the application and the features that we want to assess. In this paper, we present a computational process to select the optimal set of sensors to characterize gait asymmetries and plantar pressure patterns for stroke survivors based upon the total variation and L1 distances. The proposed mechanism is ecologically validated in a real environment with 14 stroke survivors and 14 control users. The number of sensors is reduced to 4, minimizing the cost of the device both for commercial users and companies and enhancing the cost to benefit ratio for its uptake from a national healthcare system. The results show that the sensors that better represent the gait asymmetries for healthy controls are the sensors under the big toe and midfoot and the sensors in the forefoot and midfoot for stroke survivors. The results also show that all four regions of the foot (toes, forefoot, midfoot, and heel) play an important role for plantar pressure pattern reconstruction for stroke survivors, while the heel and forefoot region are more prominent for healthy controls