Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules.

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

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Cloud computing - started as a buzz word is rapidly embraced by the enterprises and preached by the technological evangelist. Availability of high bandwidth internet at the end user level, and the adoption of virtualization for efficient resource utilization by the data-center management has given birth to this new computing paradigm. It promises colossal on-demand processing and storage capacity along with scalable service delivery model. Software solution providers are applying cloud computing to reduce service provisioning cost, by providing their business functionality as a service. However, it requires modification in context of how existing services are provisioned. Existing session management policies require dedicated computing resources to process sessions; this deviate from the concept of "Pay-As-You-Use". To conform to cloud computing architecture there is need to decouple session management with provisioned services. Derived by the need of on-demand service provisioning in this paper we present a decentralized session management framework inspired by P2P routing protocol. We call the proposed framework Chord based Session Management Framework for Software as a Service Cloud (CSMC). By applying CSMC there will be no need of separately deployed computing resources for managing sessions, in fact CSMC uses existing least utilized resources within Cloud Area Network (CAN). CSMC has been tested on three different cloud configurations, our results reveal that CSMC can be effectively deployed in cloud to achieve seamless service scalability. Additionally we have tested CSMC on different web servers to highlight its efficacy of session management on varied cloud infrastructure.

Key establishment in sensor networks is a challenging problem because existing security schemes are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. In this paper two key establishment scheme are presented using the framework of pre-distributing a random set of keys to each node. One is the random-pairwise key scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication(EG SCHEME) and other is EG SCHEME with deployment knowledge) and going to compare the basic scheme(EG SCHEME) with the deployment model(EG SCHEME with deployment knowledge).