Prem Prakash Jayaraman

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—In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is underpinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing(More)
—The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or 'things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important(More)
—Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of(More)
—Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent low-level computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use of intelligent processing such as fusing and filtering sensor data, which saves significant amount of(More)
—Data collection has always been a major challenge in sensor networks and various techniques have been proposed to enable efficient data collection. One such methodology is the use of mobile elements within the existing infrastructure to enable data collection. The paper proposes the use of existing mobile elements like mobile phones which have enough spare(More)
—Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and(More)
Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite(More)
In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms(More)