Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks

2011 ◽  
Vol 26 (1) ◽  
pp. 81-91 ◽  
Author(s):  
Sung-Han Sim ◽  
Juan Francisco Carbonell-Márquez ◽  
B.F. Spencer ◽  
Hongki Jo
2012 ◽  
Vol 204-208 ◽  
pp. 4946-4951 ◽  
Author(s):  
Ding Yu Cui ◽  
Ke Gui Xin ◽  
Billie F. Spencer ◽  
Yu Fei Liu

Wireless smart sensor networks (WSSN) have many advances compared with traditional structural health monitoring (SHM) such as wireless process, real-time calculation and low cost. However power consumption is considered as one of the most limitations in this field. The unique features offered by decentralized data aggregation (DDA) technique with the potential to overcome power consumption enable implementation of the dense array of WSSN on large structures. This paper presents a system identification of a simply supported plate based on the random decrement technique (RDT) and natural excitation technique (NExT) in combination with eigensystem realization algorithm (ERA) using Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally the system parameters including natural frequency and mode shapes are in accordance with numerical simulation showing efficacy and feasibility of decentralized NExT/ERA and RDT/ERA system identification.


2021 ◽  
Author(s):  
Van-Vi Vo ◽  
Tien-Dung Nguyen ◽  
Duc-Tai Le ◽  
Moonseong Kim ◽  
Hyunseung Choo

<div>Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.</div>


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