anomalies are detected or a threshold is reached, the monitoring system automatically transmits the information to the doctor’s workstation. Thus we are proposing a survey system which will perform these tasks by means of IoT, microcontrollers and semiconductors, sensors by means of Cloud platform.

Cloud focuses on real-time processing of interactive media applications, allowing for optimal resource planning using highly specific functional components. The proposed anomaly de-tection methods are designed to work complimentary to each other and are capable of detecting known and unknown

to evidence that the monitoring process needs to scale to thousands of heterogeneous resources at different levels (system, network, storage, application) and at different time scales; it has to cope with missing data and detect anomalies in the performance samples; it has to transform all data

to evidence that the monitoring process needs to scale to thousands of heterogeneous resources at different levels (system, network, storage, application) and at different time scales; it has to cope with missing data and detect anomalies in the performance samples; it has to transform all data

. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring

This paper proposes WIDS, a wireless intrusion detection system, which applies data mining clustering technique to wireless network data captured through hardware sensors for purposes of real time detection of anomalous behavior in wireless packets. Using hardware sensors to capture network packets

is based on three polar low cases of different types. The AMSU-B channels at 183 GHz are able to locate convective polar lows (PL) even in their incipient stage, at a time when there is considerable uncertainty as to the nature of the cloud structures seen in the visible or infrared imagery. This detection

eingereicht an der

devices in the Internet of Things (IoT) and other data sources are increasingly bridging the gap between the physical and digital world by providing fine-grained data about real-world events. Enterprise software systems are adopting the paradigm of event-based systems (EBS) to enable them to react

. This altimetric data has been derived from multi-mission satellite altimeter TOPEX, ERS-1, ERS-2, JASON-1, and ENVISAT for the period of nineteen years (1993 to 2011) using the Radar Altimeter Database System (RADS). The estimated sea level anomaly (SLA) have shown similarity in the pattern of sea level