Two major earthquakes of M L greater than 6.0 occurred in Taiwan in the first half of 2013. The vibrant shaking brought landslides, falling rocks and casualties. This paper presents a seismic network developed by National Taiwan University (NTU) with 401 Micro-Electro Mechanical System (MEMS) accelerators. The network recorded high quality strong motion signals from the two events and produced delicate shaking maps within one minute after the earthquake occurrence. The high shaking regions of the intensity map produced by the NTU system suggest damage and casualty locations. Equipped with a dense array of MEMS accelerometers, the NTU system is able to accommodate 10% signals loss from part of the seismic stations and maintain its normal functions for producing shaking maps. The system also has the potential to identify the rupture direction which is one of the key indices used to estimate possible damage. The low cost MEMS accelerator array shows its potential in real-time earthquake shaking map generation and damage avoidance.
A dense seismic network can increase Earthquake Early Warning (EEW) system capability to estimate earthquake information with higher accuracy. It is also critical for generating fast, robust earthquake alarms before strong-ground shaking hits the target area. However, building a dense seismic network via traditional seismometers is too expensive and may not be practical. Using low-cost Micro-Electro Mechanical System (MEMS) accelerometers is a potential solution to quickly deploy a large number of sensors around the monitored region. An EEW system constructed using a dense seismic network with 543 MEMS sensors in Taiwan is presented. The system also incorporates the official seismic network of Taiwan's Central Weather Bureau (CWB). The real-time data streams generated by the two networks are integrated using the Earthworm software. This paper illustrates the methods used by the integrated system for estimating earthquake information and evaluates the system performance. We applied the Earthworm picker for the seismograms recorded by the MEMS sensors (Chen et al. 2015) following new picking constraints to accurately detect P-wave arrivals and use a new regression equation for estimating earthquake magnitudes. An off-line test was implemented using 46 earthquakes with magnitudes ranging from M L 4.5 -6.5 to calibrate the system. The experimental results show that the integrated system has stable source parameter results and issues alarms much faster than the current system run by the CWB seismic network (CWBSN).
Recently, many useful services have been provided through wireless mobile devices. One of the critical issues is that mobile devices are generally built with limited computing capability, and thus may not be able to handle the complexity of many advanced services. This problem can be mitigated by offloading certain jobs from the mobile devices to the edge or cloud servers. However, the locations of the edge servers and the allocation of the offloading jobs to the edge servers can significantly affect the traffic generated in the network. Accordingly, this study conducts an investigation into the edge server placement and work allocation strategy with a focus on minimizing the total traffic load so as to achieve green communications in the mobile cloud networks. Mobile devices are assumed to be attached to the nearby node and they may send work offloading requests to the attached nodes. Meanwhile, edge servers are deployed at carefully selected nodes. The offloading jobs received by a node are assigned to a selected edge server. If the edge server is busy, the work will be forwarded to the central cloud server. Otherwise, it is processed locally in the edge server. A queuing model is adopted to evaluate the job forwarding probability at the edge servers. The problem is formulated as an integer programming problem. Two novel heuristic schemes are proposed, namely the Set-by-Set algorithm and the K-clustering algorithm. The simulation results showed that the K-clustering algorithm consistently outperforms both the Set-by-Set algorithm and the Density-Based-Clustering (DBC) algorithm presented in the literature in terms of a lower total traffic load within the network. INDEX TERMS Edge computing, cloud networks, work offloading, green communications, edge server placement.
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