Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

A novel energy-aware routing mechanism for SDN-enabled WSAN

Energy consumption is one of the most important design constraints when building a wireless sensor and actuator network since each device in the network has a limited battery capacity, and prolonging the lifetime of the network depends on saving energy. Overcoming this challenge requires a smart and reconfigurable network energy management strategy. The Software-Defined Networking (SDN) paradigm aims at building a flexible and dynamic network structure, especially in wireless sensor networks. In this study, we propose an SDN-enabled wireless sensor and actuator network architecture that has a new routing discovery mechanism. To build a flexible and energy-efficient network structure, a new routing decision approach that uses a fuzzy-based Dijkstra's algorithm is developed in the study. The proposed architecture can change the existing path during data transmission, which is the key property of our model and is achieved through the adoption of the SDN approach. All the components and algorithms of the proposed system are modeled and simulated using the Riverbed Modeler software for more realistic performance evaluation. The results indicate that the proposed SDN-enabled structure with fuzzy-based Dijkstra's algorithm outperforms the one using the regular Dijkstra's and the ZigBee-based counterpart, in terms of the energy consumption ratio, and the proposed architecture can provide an effective cluster routing while prolonging the network lifetime

A large scale SHM system: A case study on pre-stressed bridge and cloud architecture

In recent decades, external prestressing is increasingly being used especially in motorway and railway bridge structures due to the substantial savings in terms of construction time and costs. In such systems, internal and external steel tendons work together with concrete elements to withstand external actions. This means that the deterioration or failure of these elements reduces structural safety in a meaningful way. Real time monitoring of prestressing tendons can provide useful information on the health of the bridge under service loads, detecting possible fatigue, corrosion and damage/deterioration processes. However, most of the currently used structural monitoring systems are rather expensive and time consuming to install. Although many papers address high density sensing as the proper solution thanks to the “internet of things” tool, both for hardware and software, there are not so many applications in which this approach is really put into service. This paper describes the application of MEMS accelerometers in a high performance and cost-effective SHM system for bridge structures. In particular, data from a real time monitoring system installed in a box section composite highway bridge are presented. The external tendons of this bridge have been instrumented with a total number of 88 triaxial accelerometers. Changes in the dynamic characteristics of the monitored elements have been analyzed by detecting the shift in tendons’ dynamic behavior. The main challenge was collecting a huge amount of data and find a way to properly process them, not requiring the operator’s direct action, unless the observed situation is out of the “normal” scenario. For this purpose, simple but easy-to-implement specific data processing algorithms have been tested in order to check the real feasibility of such a SHM system first, and then to analyze the collected sensor data and provide an efficient real time damage detection