ABSTRACT
Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network's performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols' performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.
ABSTRACT
We put forward a robust technique to encode a plethora of arbitrary images as nondiffracting beams by optimizing their respective phase components. This technique works directly under the constraint of a ring of infinitesimal width in Fourier space. The procedure reported is based on a stochastic direct search and global optimization: the differential evolution method. Unlike previous methods reported, the present approach is also able to optimize the spatial frequency of the image used. Remarkably, this technique also demonstrates that it is possible to encode even more arbitrary images on demand into nondiffracting forms by allowing a segmentation of the profile. We provide some codes to generate nondiffracting beams by using this algorithm.
