Yaser Jararweh

In this paper we present a novel cloud supported model for efficient community health awareness in presence of a large scale WBANs data generation. The objective is to process this big data in order to detect the abnormal data using MapReduce infrastructure and user defined functions with minimum processing delay. The goal is to have a large monitored data of WBANs to be available to the end user or to the decision maker in reliable manner. The proposed work is minimizing the data processing delay by choosing cloudlet or local cloud model and MapReduce infrastructure. So, the overall delay is minimized, thus leading to detect the abnormal data in the cloud in real time mode. Performance results show that integrating the MapReduce capabilities with cloud computing model will significantly reduce the processing delay.

This paper presents an efficient large scale data collection in Wireless Body Area Network (WBANs) in the presence of cloudlet-based prototype system. The key contribution of this paper is to collect the observed data of WBANs in a large scale and convey it in consistent manner to the other end of service providers. A model of WBANs is proposed in this work including virtualized machines and Cloudlet in order to characterize the efficient WBANs data collection. A scalable storage and processing infrastructure have been proposed to support large scale WBANs system, which is efficiently capable to handle the big data generated by large number of WBANs users. The proposed model supports effective cost communication technologies through Wi-Fi technology. Performance results of the proposed prototype are evaluated using advanced CloudSim simulator. The performance results show that the consumed power and packet delay of the collected data is decreased by increasing the number virtualized...

Most of the security related research for cloud computing focuses on attacks that are generated outside the cloud system and aims to gain unauthorized access to the cloud resources and data. However, the insider attackers are more challenging and can cause a severe impact on the cloud system stability and quality of service. In this paper, we propose a novel insider threat prevention model using the knowledgebase prevention approaches. Knowledgebase models were used before in preventing insider threats in relational databases systems. In this work, we extend this work to critical cloud computing system. The proposed model will insure an early detection (and hence, the prevention) of possible insider breaches by correlating the system admins knowledge that may grant undesired privileges to the insiders. The incurred complexity by the model will be tolerated and will not impact the system quality of service. When tackling the security issues of insiders within the cloud, the security ...

The analysis of large-scale data for the purpose of extracting patterns is applicable to several research fields. However, the size of this data is rapidly growing on a daily basis creating a need for new computing paradigms capable of handling such growing data efficiently. Cloud computing is one of the possible solutions to satisfy this pressing need. In this paper, a cloud computing based study for large scale climate related historical data from Jordan is conducted. The main focus is to accelerate the experimental part of the climate research using the cloud computing Infrastructure which will lead to faster results generation. Climate change research is receiving a lot of interest as the climate change phenomena is expected to have a direct as well as an indirect impact on human life. However, the amount of computational resources required to conduct such research in a useful and practical manner is very high. This work aims at accelerating the climate changes related research ...

ABSTRACT The needs for efficient and scalable community health awareness model become a crucial issue in today’s health care applications. Many health care service providers need to provide their services for long terms, in real time and interactively. Many of these applications are based on the emerging Wireless Body Area networks (WBANs) technology. WBANs have developed as an effective solution for a wide range of healthcare, military, sports, general health and social applications. On the other hand, handling data in a large scale (currently known as Big Data) requires an efficient collection and processing model with scalable computing and storage capacity. Therefore, a new computing paradigm is needed such as Cloud Computing and Internet of Things (IoT). In this paper we present a novel cloud supported model for efficient community health awareness in presence of a large scale WBANs data generation. The objective is to process this big data in order to detect the abnormal data using MapReduce infrastructure and user defined functions with minimum processing delay. The goal is to have a large monitored data of WBANs to be available to the end user or to the decision maker in reliable manner. While reducing data packet processing energy, the proposed work is minimizing the data processing delay by choosing cloudlet or local cloud model and MapReduce infrastructure. So, the overall delay is minimized, thus leading to detect the abnormal data in the cloud in real time mode. In this paper we present a multi-layer computing model composed of Local Cloud (LC) layer and Enterprise Cloud (EP) layer that aim to process the collected data from Monitored Subjects (MSs) in a large scale to generate useful facts, observations or to find abnormal phenomena within the monitored data. Performance results show that integrating the MapReduce capabilities with cloud computing model will reduce the processing delay. The proposed MapReduce infrastructure has been also applied in lower layer, such as LC in order to reduce the amount of communications and processing delay. Performance results show that applying MapReduce infrastructure in lower tire will significantly decrease the overall processing delay.