A Tier-Based Duty-Cycling Scheme for Forest Monitoring

Abstract

Wireless sensor networks for forest monitoring are typically deployed in fields in which manual intervention cannot be easily accessed. An interesting approach to extending the lifetime of sensor nodes is the use of energy harvested from the environment. Design constraints are application-dependent and based on the monitored environment in which the energy harvesting takes place. To reduce energy consumption, we designed a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time. The dynamic duty cycle scheduling is realized based on a tier structure in which the network is concentrically organized around the sink node. In addition, the multi-paths preserved in the tier structure can be used to deliver residual packets when a path failure occurs. Experimental results show that the proposed method has a better performance.

Autoencoder-based candidate waypoint generation method for autonomous flight of multi-unmanned aerial vehicles

“…Moreover, a method will be developed to plan a flight Figure 14. Results based on the amount of learning data: (a) overlapped pixels of accumulated image I 1 and reconstructed accumulated image I' 1 , (b) overlapped pixels of accumulated image I 2 and reconstructed accumulated image I' 2 , (c) overlapped pixels of accumulated image I 3 and reconstructed accumulated image I' 3 , (d) overlapped pixels of accumulated image I 4 and reconstructed accumulated image I' 4 , (e) overlapped pixels of accumulated image I 5 and reconstructed accumulated image I' 5 , and (e) overlapped pixels of accumulated image I 6 and reconstructed accumulated image I' 6 . Figure 13.…”

“…Unmanned aerial vehicles (UAVs) 1 autonomously fly using onboard cameras and perform monitoring functions such as traffic regulation using onboard cameras. [2][3][4][5] A pilot can control each UAV by watching UAV camera screens and considering UAV positions. 6,7 However, it is difficult for a single pilot to control UAVs concurrently.…”

Mie Sensing with Neural Networks: Recognition of Nano‐Object Parameters, the Invisibility Point, and Restricted Models

“…[21] Along with treating purely optical responses, the ML approaches and recognition formalisms can be applied in the research fields related to the hot-electron effects [35] and photothermal phenomena. [36] Nowadays, neural networks have found a large number of applications, for example, natural language processing, [37] image recognition, [38,39] super resolution microscopy, [40,41] design and optimization of nanophotonic devices. [26,42,43] However, the recognition of the nano-objects in solution remains a challenging problem with a relevant history in nanophotonics (see section 1.5 in Ref.…”

Efficient Approaches to Computer Vision and Pattern Recognition

“…Note, however, that the limited power of the wireless sensor nodes restricts the development and application of WSN, which requires a very long lifetime to improve performance [29]. Zhang et al [30] suggested a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time to reduce energy consumption. Dynamic duty cycle scheduling is based on a tier structure wherein the network is concentrically organized around the sink node.…”