Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
Smart structures mimic biological systems by using thousands of sensors serving as\na nervous system analog. One approach to give structures this sensing ability is to develop a\nmultifunctional sensor network. Previous work has demonstrated stretchable sensor networks\nconsisting of temperature sensors and impact detectors for monitoring external environments and\ninteracting with other objects. The objective of this work is to develop distributed, robust and reliable\nstrain gauges for obtaining the strain distribution of a designated region on the target structure. Here,\nwe report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices\nand 8 piezoelectric transducers symmetrically distributed over an area of 150 Ã? 150 mm to map\nand quantify multiple physical stimuli with a spatial resolution of 2.5 Ã? 2.5 mm. We performed\ncomputational modeling of the network stretching process to improve measurement accuracy and\nconducted experimental characterizations of the microfabricated strain gauges to verify their gauge\nfactor and temperature coefficient. Collectively, the results represent a robust and reliable sensing\nsystem that is able to generate a distributed strain profile of a common structure. The reported\nstrain gauge network may find a wide range of applications in morphing wings, smart buildings,\nautonomous cars and intelligent robots....
Wireless rechargeable sensor nodes can collect additional data, which leads to an increase\nin the precision of data analysis, when enough harvested energy is acquired. However, because\nsuch nodes increase the amount of sensory data, some nodes (especially near the sink) may\nblackout because more transmitted data can make relaying nodes expend more energy. In this\npaper, we propose an energy-aware control scheme of data compression and sensing rate to maximize\nthe amount of data collected at the sink, while minimizing the blackout time. In this scheme,\neach dominant node determines the data quota that all its descendant nodes can transmit during\nthe next period, which operates with an efficient energy allocation scheme. Then, the node receiving\nthe quota selects an appropriate data compression algorithm and sensing rate according to both its\nquota and allocated energy during the next period, so as not to exhaust the energy of nodes near the\nsink. Experimental results verify that the proposed scheme collects more data than other schemes,\nwhile suppressing the blackout of nodes. We also found that it adapts better to changes in node\ndensity and harvesting environments....
In this paper, the blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless\nsensor networks (WSNs). In our system, the nodes storing the data are rewarded with digital money. The more the data stored by\nthe node, the more the reward it achieves. Moreover, two blockchains are constructed. One is utilized to store data of each node\nand another is to control the access of data. In addition, our proposal adopts the provable data possession to replace the proof of\nwork (PoW) in original bitcoins to carry out the mining and storage of new data blocks, which greatly reduces the computing\npower comparing to the PoW mechanism. Furthermore, the preserving hash functions are used to compare the stored data and\nthe new data block. The new data can be stored in the node which is closest to the existing data, and only the different sub blocks are\nstored. Thus, it can greatly save the storage space of network nodes....
The problem of target localization in WSN (wireless sensor network) has received much\nattention in recent years. However, the performance of traditional localization algorithms will\ndrastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have\nbeen presented to address this issue, such as the optimization-based method and the NLOS modeling\nmethod. The former produces a higher complexity and the latter is sensitive to the propagating\nenvironment. Therefore, this paper puts forward a simple NLOS identification and localization\nalgorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor\nnodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can\nget intermediate position estimates of target node through traditional localization algorithms. Then,\nthe AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based\nhypothesis test, where the test variable, i.e., the localization residual, is computed according to the\nintermediate position estimations. Finally, the position of target node can be estimated by only using\nANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm\ncan successfully identify the NLOS-AN, by which the following localization produces high accuracy\nso long as there are no less than two LOS-ANs....
A low-power wireless sensor/actuator network was specially developed and optimized for piezoceramic transducer-based active\nsensing applications. Wireless sensor network promises increased system flexibility, lower system cost, and increased robustness\nthrough decentralization. Piezoceramic signal conditioning circuit, actuating circuit, power management, and wireless\nmicro controller were integrated in the hardware design. IEEE 802.15.4 wireless stack protocol was implemented on the\nhardware, and user input/output management together with a shell provided easier debugging and configuring interface. The\ndesigned system provides a low-power wireless solution towards many applications such as wireless structural health monitoring\nand wireless structural vibration control....
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